目录
1. An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation [PDF] 摘要
2. A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus [PDF] 摘要
3. Sentence Analogies: Exploring Linguistic Relationships and Regularities in Sentence Embeddings [PDF] 摘要
5. Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences [PDF] 摘要
6. Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification [PDF] 摘要
8. The growing echo chamber of social media: Measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009--2020 [PDF] 摘要
10. Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes [PDF] 摘要
12. General-Purpose Communicative Function Recognition using a Hierarchical Network with Cascading Outputs and Maximum a Posteriori Path Estimation [PDF] 摘要
14. A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier [PDF] 摘要
19. Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA [PDF] 摘要
摘要
1. An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation [PDF] 返回目录
Piji Li
Abstract: We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct the parameter learning. Corpora of News and Wikipedia in Chinese and English are collected for the pre-training stage respectively. Dialogue context and response are concatenated into a single sequence utilized as the input of the models during the fine-tuning stage. A weighted joint prediction paradigm for both context and response is designed to evaluate the performance of models with or without the loss term for context prediction. Various of decoding strategies such as greedy search, beam search, top-k sampling, etc. are employed to conduct the response text generation. Extensive experiments are conducted on the typical single-turn and multi-turn dialogue corpora such as Weibo, Douban, Reddit, DailyDialog, and Persona-Chat. Detailed numbers of automatic evaluation metrics on relevance and diversity of the generated results for the languages models as well as the baseline approaches are reported.
摘要：我们提出的预先训练基于变压器的自回归语言模型的开放域对话一代人的重任的实证调查。预训练和微调培训范式被用来进行参数学习。新闻与维基百科在中国和英语语料库分别收集预训练阶段。对话上下文和响应连接成用作模型的过程中微调级的输入的单个序列。两个上下文和响应的加权预测关节范例是设计用于评估的模型有或没有上下文预测损耗项的性能。各种解码策略如贪婪搜索，波束搜索，前k个采样等的被采用以进行响应文本生成。广泛实验在典型的单匝和多匝对话语料库如微博，豆瓣，书签交易，DailyDialog，和人物角色的聊天进行。报告对语言模型生成的结果的相关性和多样性自动评估指标以及基线方法的详细数字。
Piji Li
Abstract: We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct the parameter learning. Corpora of News and Wikipedia in Chinese and English are collected for the pre-training stage respectively. Dialogue context and response are concatenated into a single sequence utilized as the input of the models during the fine-tuning stage. A weighted joint prediction paradigm for both context and response is designed to evaluate the performance of models with or without the loss term for context prediction. Various of decoding strategies such as greedy search, beam search, top-k sampling, etc. are employed to conduct the response text generation. Extensive experiments are conducted on the typical single-turn and multi-turn dialogue corpora such as Weibo, Douban, Reddit, DailyDialog, and Persona-Chat. Detailed numbers of automatic evaluation metrics on relevance and diversity of the generated results for the languages models as well as the baseline approaches are reported.
摘要：我们提出的预先训练基于变压器的自回归语言模型的开放域对话一代人的重任的实证调查。预训练和微调培训范式被用来进行参数学习。新闻与维基百科在中国和英语语料库分别收集预训练阶段。对话上下文和响应连接成用作模型的过程中微调级的输入的单个序列。两个上下文和响应的加权预测关节范例是设计用于评估的模型有或没有上下文预测损耗项的性能。各种解码策略如贪婪搜索，波束搜索，前k个采样等的被采用以进行响应文本生成。广泛实验在典型的单匝和多匝对话语料库如微博，豆瓣，书签交易，DailyDialog，和人物角色的聊天进行。报告对语言模型生成的结果的相关性和多样性自动评估指标以及基线方法的详细数字。
2. A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus [PDF] 返回目录
Tobias Daudert
Abstract: We introduce FinLin, a novel corpus containing investor reports, company reports, news articles, and microblogs from StockTwits, targeting multiple entities stemming from the automobile industry and covering a 3-month period. FinLin was annotated with a sentiment score and a relevance score in the range [-1.0, 1.0] and [0.0, 1.0], respectively. The annotations also include the text spans selected for the sentiment, thus, providing additional insight into the annotators' reasoning. Overall, FinLin aims to complement the current knowledge by providing a novel and publicly available financial sentiment corpus and to foster research on the topic of financial sentiment analysis and potential applications in behavioural science.
摘要：我们从StockTwits介绍FinLin，含投资者报告，公司报告，新闻文章的新颖语料库和微博，针对来自汽车行业所产生和覆盖3个月内的多个实体。 FinLin用情绪分数和一个相关性得分的范围为[-1.0，1.0]和[0.0，1.0]分别注释。注释还包括选择用于情绪文本跨距，因而，提供额外洞察注释的推理。总体而言，FinLin旨在通过提供一种新颖的和公开的财务情绪语料库，并就金融情感分析和行为科学的潜在应用的课题研究培育，以补充目前的知识。
Tobias Daudert
Abstract: We introduce FinLin, a novel corpus containing investor reports, company reports, news articles, and microblogs from StockTwits, targeting multiple entities stemming from the automobile industry and covering a 3-month period. FinLin was annotated with a sentiment score and a relevance score in the range [-1.0, 1.0] and [0.0, 1.0], respectively. The annotations also include the text spans selected for the sentiment, thus, providing additional insight into the annotators' reasoning. Overall, FinLin aims to complement the current knowledge by providing a novel and publicly available financial sentiment corpus and to foster research on the topic of financial sentiment analysis and potential applications in behavioural science.
摘要：我们从StockTwits介绍FinLin，含投资者报告，公司报告，新闻文章的新颖语料库和微博，针对来自汽车行业所产生和覆盖3个月内的多个实体。 FinLin用情绪分数和一个相关性得分的范围为[-1.0，1.0]和[0.0，1.0]分别注释。注释还包括选择用于情绪文本跨距，因而，提供额外洞察注释的推理。总体而言，FinLin旨在通过提供一种新颖的和公开的财务情绪语料库，并就金融情感分析和行为科学的潜在应用的课题研究培育，以补充目前的知识。
3. Sentence Analogies: Exploring Linguistic Relationships and Regularities in Sentence Embeddings [PDF] 返回目录
Xunjie Zhu, Gerard de Melo
Abstract: While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.
摘要：虽然字向量表示重要的特性已被广泛研究，至今却知之甚少句子向量表示的属性。词矢量经常被评估到什么程度，他们表现出相对于排序的字类比考虑的关系规律进行评估。在本文中，我们调查到什么程度常用的句子向量表示空间以及反映某些类型的规律性的东西。我们提出了一些方案来诱导评价数据的基础上，词汇类比数据，以及句子之间的语义关系。我们的实验考虑各种各样的句子嵌入方法，包括基于BERT式上下文的嵌入的。我们发现，不同的模型在他们反映这种规律的能力显着不同。
Xunjie Zhu, Gerard de Melo
Abstract: While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations. Word vectors are often evaluated by assessing to what degree they exhibit regularities with regard to relationships of the sort considered in word analogies. In this paper, we investigate to what extent commonly used sentence vector representation spaces as well reflect certain kinds of regularities. We propose a number of schemes to induce evaluation data, based on lexical analogy data as well as semantic relationships between sentences. Our experiments consider a wide range of sentence embedding methods, including ones based on BERT-style contextual embeddings. We find that different models differ substantially in their ability to reflect such regularities.
摘要：虽然字向量表示重要的特性已被广泛研究，至今却知之甚少句子向量表示的属性。词矢量经常被评估到什么程度，他们表现出相对于排序的字类比考虑的关系规律进行评估。在本文中，我们调查到什么程度常用的句子向量表示空间以及反映某些类型的规律性的东西。我们提出了一些方案来诱导评价数据的基础上，词汇类比数据，以及句子之间的语义关系。我们的实验考虑各种各样的句子嵌入方法，包括基于BERT式上下文的嵌入的。我们发现，不同的模型在他们反映这种规律的能力显着不同。
4. Shallow Discourse Annotation for Chinese TED Talks [PDF] 返回目录
Wanqiu Long, Xinyi Cai, James E. M. Reid, Bonnie Webber, Deyi Xiong
Abstract: Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.
摘要：与语言相关的属性注释文本语料库的语言技术发展的重要资源。当前的工作有助于为中国语言技术，为中国 - 英语的新资源，以一套TED演讲的形式（有些是英文原来给定的，有的在中国）已经标注了在的风格话语关系宾州树库话语，适用于不存在英语中国文字的性质。资源目前在注释计划口语独白话语级属性，而不是书面文字的独特。安标注间协议的研究表明，注释方案能够实现高度可靠的结果。
Wanqiu Long, Xinyi Cai, James E. M. Reid, Bonnie Webber, Deyi Xiong
Abstract: Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.
摘要：与语言相关的属性注释文本语料库的语言技术发展的重要资源。当前的工作有助于为中国语言技术，为中国 - 英语的新资源，以一套TED演讲的形式（有些是英文原来给定的，有的在中国）已经标注了在的风格话语关系宾州树库话语，适用于不存在英语中国文字的性质。资源目前在注释计划口语独白话语级属性，而不是书面文字的独特。安标注间协议的研究表明，注释方案能够实现高度可靠的结果。
5. Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences [PDF] 返回目录
Petar Milin, Harish Tayyar Madabushi, Michael Croucher, Dagmar Divjak
Abstract: In this paper we present the Widrow-Hoff rule and its applications to language data. After contextualizing the rule historically and placing it in the chain of neurally inspired artificial learning models, we explain its rationale and implementational considerations. Using a number of case studies we illustrate how the Widrow-Hoff rule offers unexpected opportunities for the computational simulation of a range of language phenomena that make it possible to approach old problems from a novel perspective.
摘要：在本文中，我们目前的的Widrow - 霍夫规律及其应用的语言数据。历史情境的规则，将其放置在neurally启发人工学习模型的链条后，我们解释其理由和实施过程中的注意事项。根据大量的案例研究，我们说明的Widrow - 霍夫规则如何对一系列的语言现象，使人们可以从一个新的角度来处理老问题的计算机模拟提供了意想不到的机会。
Petar Milin, Harish Tayyar Madabushi, Michael Croucher, Dagmar Divjak
Abstract: In this paper we present the Widrow-Hoff rule and its applications to language data. After contextualizing the rule historically and placing it in the chain of neurally inspired artificial learning models, we explain its rationale and implementational considerations. Using a number of case studies we illustrate how the Widrow-Hoff rule offers unexpected opportunities for the computational simulation of a range of language phenomena that make it possible to approach old problems from a novel perspective.
摘要：在本文中，我们目前的的Widrow - 霍夫规律及其应用的语言数据。历史情境的规则，将其放置在neurally启发人工学习模型的链条后，我们解释其理由和实施过程中的注意事项。根据大量的案例研究，我们说明的Widrow - 霍夫规则如何对一系列的语言现象，使人们可以从一个新的角度来处理老问题的计算机模拟提供了意想不到的机会。
6. Pseudo Labeling and Negative Feedback Learning for Large-scale Multi-label Domain Classification [PDF] 返回目录
Joo-Kyung Kim, Young-Bum Kim
Abstract: In large-scale domain classification, an utterance can be handled by multiple domains with overlapped capabilities. However, only a limited number of ground-truth domains are provided for each training utterance in practice while knowing as many as correct target labels is helpful for improving the model performance. In this paper, given one ground-truth domain for each training utterance, we regard domains consistently predicted with the highest confidences as additional pseudo labels for the training. In order to reduce prediction errors due to incorrect pseudo labels, we leverage utterances with negative system responses to decrease the confidences of the incorrectly predicted domains. Evaluating on user utterances from an intelligent conversational system, we show that the proposed approach significantly improves the performance of domain classification with hypothesis reranking.
摘要：在大规模域分类，话语可以通过与重叠能力的多个域来处理。但是，提供给每个训练发言在实践中，只有地面实况域的数量有限，而明知多达正确目标的标签是提高模型的性能很有帮助。在本文中，给出了每个训练发言一个地面实况领域，我们认为具有最高的可信度作为附加伪标签的训练一直预测域。为了减少由于不正确的伪标签预测误差，我们利用负的系统响应的话语来降低错误预测域的可信度。从智能对讲系统用户的话语评价，我们证明了该方法显著改善域分类与假设重新排名的性能。
Joo-Kyung Kim, Young-Bum Kim
Abstract: In large-scale domain classification, an utterance can be handled by multiple domains with overlapped capabilities. However, only a limited number of ground-truth domains are provided for each training utterance in practice while knowing as many as correct target labels is helpful for improving the model performance. In this paper, given one ground-truth domain for each training utterance, we regard domains consistently predicted with the highest confidences as additional pseudo labels for the training. In order to reduce prediction errors due to incorrect pseudo labels, we leverage utterances with negative system responses to decrease the confidences of the incorrectly predicted domains. Evaluating on user utterances from an intelligent conversational system, we show that the proposed approach significantly improves the performance of domain classification with hypothesis reranking.
摘要：在大规模域分类，话语可以通过与重叠能力的多个域来处理。但是，提供给每个训练发言在实践中，只有地面实况域的数量有限，而明知多达正确目标的标签是提高模型的性能很有帮助。在本文中，给出了每个训练发言一个地面实况领域，我们认为具有最高的可信度作为附加伪标签的训练一直预测域。为了减少由于不正确的伪标签预测误差，我们利用负的系统响应的话语来降低错误预测域的可信度。从智能对讲系统用户的话语评价，我们证明了该方法显著改善域分类与假设重新排名的性能。
7. Investigating the Decoders of Maximum Likelihood Sequence Models: A Look-ahead Approach [PDF] 返回目录
Yu-Siang Wang, Yen-Ling Kuo, Boris Katz
Abstract: We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to consider the likelihood information of a rollout up to k steps. Unlike other approaches that need to train another value network to evaluate the rollouts, we can directly apply this look-ahead module to improve the decoding of any sequence model trained in a maximum likelihood framework. We evaluate our look-ahead module on three datasets of varying difficulties: IM2LATEX-100k OCR image to LaTeX, WMT16 multimodal machine translation, and WMT14 machine translation. Our look-ahead module improves the performance of the simpler datasets such as IM2LATEX-100k and WMT16 multimodal machine translation. However, the improvement of the more difficult dataset (e.g., containing longer sequences), WMT14 machine translation, becomes marginal. Our further investigation using the k-step look-ahead suggests that the more difficult tasks suffer from the overestimated EOS (end-of-sentence) probability. We argue that the overestimated EOS probability also causes the decreased performance of beam search when increasing its beam width. We tackle the EOS problem by integrating an auxiliary EOS loss into the training to estimate if the model should emit EOS or other words. Our experiments show that improving EOS estimation not only increases the performance of our proposed look-ahead module but also the robustness of the beam search.
摘要：我们证明我们如何能够切实纳入多步未来的信息到最大似然序列模型的解码器。我们提出了一个“K-一步先行”模块考虑推出多达k步的可能性的信息。不同于需要培训的另一个价值网络评估的推出，我们可以直接将此预测模块，以提高最大似然框架训练有素的任何序列模型的解码其他方法。我们评估我们对不同的困难三个数据集预测模块：IM2LATEX-100K OCR图像乳胶，WMT16多式联运机器翻译，和WMT14机器翻译。我们预测模块提高了简单的数据集，如IM2LATEX-100K和WMT16多式联运机器翻译的性能。然而，更困难的数据集的改善（例如，包含更长的序列），WMT14机器翻译，变为边际。我们使用K-一步前瞻进一步的调查表明，更为艰巨的任务由过度估计的EOS（结束句）的概率受到影响。我们认为，过度估计EOS概率也导致束搜索的性能下降增加其波束宽度时。我们通过整合辅助EOS损失投入到训练中来估算，如果模型应该发出EOS或者换句话说解决EOS问题。我们的实验表明，提高EOS估计不仅增加了我们提出的预测模块的性能，而且光束搜索的鲁棒性。
Yu-Siang Wang, Yen-Ling Kuo, Boris Katz
Abstract: We demonstrate how we can practically incorporate multi-step future information into a decoder of maximum likelihood sequence models. We propose a "k-step look-ahead" module to consider the likelihood information of a rollout up to k steps. Unlike other approaches that need to train another value network to evaluate the rollouts, we can directly apply this look-ahead module to improve the decoding of any sequence model trained in a maximum likelihood framework. We evaluate our look-ahead module on three datasets of varying difficulties: IM2LATEX-100k OCR image to LaTeX, WMT16 multimodal machine translation, and WMT14 machine translation. Our look-ahead module improves the performance of the simpler datasets such as IM2LATEX-100k and WMT16 multimodal machine translation. However, the improvement of the more difficult dataset (e.g., containing longer sequences), WMT14 machine translation, becomes marginal. Our further investigation using the k-step look-ahead suggests that the more difficult tasks suffer from the overestimated EOS (end-of-sentence) probability. We argue that the overestimated EOS probability also causes the decreased performance of beam search when increasing its beam width. We tackle the EOS problem by integrating an auxiliary EOS loss into the training to estimate if the model should emit EOS or other words. Our experiments show that improving EOS estimation not only increases the performance of our proposed look-ahead module but also the robustness of the beam search.
摘要：我们证明我们如何能够切实纳入多步未来的信息到最大似然序列模型的解码器。我们提出了一个“K-一步先行”模块考虑推出多达k步的可能性的信息。不同于需要培训的另一个价值网络评估的推出，我们可以直接将此预测模块，以提高最大似然框架训练有素的任何序列模型的解码其他方法。我们评估我们对不同的困难三个数据集预测模块：IM2LATEX-100K OCR图像乳胶，WMT16多式联运机器翻译，和WMT14机器翻译。我们预测模块提高了简单的数据集，如IM2LATEX-100K和WMT16多式联运机器翻译的性能。然而，更困难的数据集的改善（例如，包含更长的序列），WMT14机器翻译，变为边际。我们使用K-一步前瞻进一步的调查表明，更为艰巨的任务由过度估计的EOS（结束句）的概率受到影响。我们认为，过度估计EOS概率也导致束搜索的性能下降增加其波束宽度时。我们通过整合辅助EOS损失投入到训练中来估算，如果模型应该发出EOS或者换句话说解决EOS问题。我们的实验表明，提高EOS估计不仅增加了我们提出的预测模块的性能，而且光束搜索的鲁棒性。
8. The growing echo chamber of social media: Measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009--2020 [PDF] 返回目录
Thayer Alshaabi, David R. Dewhurst, Joshua R. Minot, Michael V. Arnold, Jane L. Adams, Christopher M. Danforth, Peter Sheridan Dodds
Abstract: Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, and Portuguese being the most dominant. To quantify each language's level of being a Twitter `echo chamber' over time, we compute the `contagion ratio': the balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1---the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
摘要：从工作中从2009年开始到2019年年底运行118个十亿消息的数据集，我们确定和探讨在Twitter上的相对日常使用超过150种语言的。我们发现，八国语言包括所有的鸣叫的80％，有英语，日语，西班牙语和葡萄牙语是最占优势。量化是一个Twitter`回声室的每种语言的水平“随着时间的推移，我们计算了`传染比”：锐推的有机消息的平衡。我们发现，对于Twitter上最常用的语言有一种日益增长的趋势，虽然不是万能，进行转发，而不是份额的新内容。截至2019年年底，排名前30种语言，包括英语和西班牙语的一半的传染率，也高于1达到---天真的蔓延阈值。在2019年，前5种语言的最高日均率分别为，为了，泰国（7.3），印地文，泰米尔语，乌尔都语和加泰罗尼亚，而下方5是俄语，瑞典语，世界语，宿务，和芬兰（0.26） 。此外，我们表明，随着时间的推移，对于大多数常用的语言的传染率更强烈比那些稀有语种的增长。
Thayer Alshaabi, David R. Dewhurst, Joshua R. Minot, Michael V. Arnold, Jane L. Adams, Christopher M. Danforth, Peter Sheridan Dodds
Abstract: Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, and Portuguese being the most dominant. To quantify each language's level of being a Twitter `echo chamber' over time, we compute the `contagion ratio': the balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1---the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.
摘要：从工作中从2009年开始到2019年年底运行118个十亿消息的数据集，我们确定和探讨在Twitter上的相对日常使用超过150种语言的。我们发现，八国语言包括所有的鸣叫的80％，有英语，日语，西班牙语和葡萄牙语是最占优势。量化是一个Twitter`回声室的每种语言的水平“随着时间的推移，我们计算了`传染比”：锐推的有机消息的平衡。我们发现，对于Twitter上最常用的语言有一种日益增长的趋势，虽然不是万能，进行转发，而不是份额的新内容。截至2019年年底，排名前30种语言，包括英语和西班牙语的一半的传染率，也高于1达到---天真的蔓延阈值。在2019年，前5种语言的最高日均率分别为，为了，泰国（7.3），印地文，泰米尔语，乌尔都语和加泰罗尼亚，而下方5是俄语，瑞典语，世界语，宿务，和芬兰（0.26） 。此外，我们表明，随着时间的推移，对于大多数常用的语言的传染率更强烈比那些稀有语种的增长。
9. Multi-task Learning Based Neural Bridging Reference Resolution [PDF] 返回目录
Juntao Yu, Massimo Poesio
Abstract: We propose a multi task learning-based neural model for bridging reference resolution tackling two key challenges faced by bridging reference resolution. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied easily to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 34.9 percentage points.
摘要：我们提出了弥合参考分辨率应对所面临的桥接参考决议的两个关键挑战为基础的学习任务多神经网络模型。第一个挑战是缺乏具有桥接的引用注释大语料库。为了解决这个问题，我们采用多任务学习有帮助弥合参考分辨率指代消解。我们显示的是实质性的改进，8 P.P.可以在全分辨率桥接这种架构来实现。第二个挑战是在不同的语料库桥接使用的定义不同，采用特殊的功能设计为一个文集不与其他语料库很好地工作意味着手工编码的系统或系统。我们的神经元模型仅使用少量的语料库独立特征，从而可以容易地适用于不同的语料库。具有非常不同的桥接语料库评估（阿劳，ISNOTES，巴士和SCICORP）认为，我们的架构同样适用于所有的语料，实现对全桥分辨率SOTA结果全部语料，跑赢最多最好的业绩报告34.9个百分点。 。
Juntao Yu, Massimo Poesio
Abstract: We propose a multi task learning-based neural model for bridging reference resolution tackling two key challenges faced by bridging reference resolution. The first challenge is the lack of large corpora annotated with bridging references. To address this, we use multi-task learning to help bridging reference resolution with coreference resolution. We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture. The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora. Our neural model only uses a small number of corpus independent features, thus can be applied easily to different corpora. Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 34.9 percentage points.
摘要：我们提出了弥合参考分辨率应对所面临的桥接参考决议的两个关键挑战为基础的学习任务多神经网络模型。第一个挑战是缺乏具有桥接的引用注释大语料库。为了解决这个问题，我们采用多任务学习有帮助弥合参考分辨率指代消解。我们显示的是实质性的改进，8 P.P.可以在全分辨率桥接这种架构来实现。第二个挑战是在不同的语料库桥接使用的定义不同，采用特殊的功能设计为一个文集不与其他语料库很好地工作意味着手工编码的系统或系统。我们的神经元模型仅使用少量的语料库独立特征，从而可以容易地适用于不同的语料库。具有非常不同的桥接语料库评估（阿劳，ISNOTES，巴士和SCICORP）认为，我们的架构同样适用于所有的语料，实现对全桥分辨率SOTA结果全部语料，跑赢最多最好的业绩报告34.9个百分点。 。
10. Discovering linguistic (ir)regularities in word embeddings through max-margin separating hyperplanes [PDF] 返回目录
Noel Kennedy, Imogen Schofield, Dave C. Brodbelt, David B. Church, Dan G. O'Neill
Abstract: We experiment with new methods for learning how related words are positioned relative to each other in word embedding spaces. Previous approaches learned constant vector offsets: vectors that point from source tokens to target tokens with an assumption that these offsets were parallel to each other. We show that the offsets between related tokens are closer to orthogonal than parallel, and that they have low cosine similarities. We proceed by making a different assumption; target tokens are linearly separable from source and un-labeled tokens. We show that a max-margin hyperplane can separate target tokens and that vectors orthogonal to this hyperplane represent the relationship between source and targets. We find that this representation of the relationship obtains the best results in dis-covering linguistic regularities. We experiment with vector space models trained by a variety of algorithms (Word2vec: CBOW/skip-gram, fastText, or GloVe), and various word context choices such as linear word-order, syntax dependency grammars, and with and without knowledge of word position. These experiments show that our model, SVMCos, is robust to a range of experimental choices when training word embeddings.
摘要：我们与学习的话如何与在Word中嵌入空间相对彼此定位成新的方法进行实验。先前的方法学习常数向量偏移：矢量，从源点到令牌目标令牌与一个假设，即这些偏移量是彼此平行。我们表明，相关的标记之间的偏移量接近正交比并行，并且它们具有低余弦相似之处。我们继续通过使不同的假设;目标标记是由源和未标记的令牌线性可分。我们表明，一个最大利润的超平面可以分离目标令牌和该矢量正交的这个超平面表示源和目标之间的关系。我们发现，这种关系的这种表示在获得存款保险计划覆盖的语言规律的最好成绩。我们用由各种算法训练向量空间模型进行试验（Word2vec：CBOW /跳过克，fastText，或手套），以及各种单词的上下文的选择，如线性词序，语法依赖性文法，和有和没有字的知识位置。这些实验表明我们的模型，SVMCos，是稳健的一系列实验选择时的训练字的嵌入的。
Noel Kennedy, Imogen Schofield, Dave C. Brodbelt, David B. Church, Dan G. O'Neill
Abstract: We experiment with new methods for learning how related words are positioned relative to each other in word embedding spaces. Previous approaches learned constant vector offsets: vectors that point from source tokens to target tokens with an assumption that these offsets were parallel to each other. We show that the offsets between related tokens are closer to orthogonal than parallel, and that they have low cosine similarities. We proceed by making a different assumption; target tokens are linearly separable from source and un-labeled tokens. We show that a max-margin hyperplane can separate target tokens and that vectors orthogonal to this hyperplane represent the relationship between source and targets. We find that this representation of the relationship obtains the best results in dis-covering linguistic regularities. We experiment with vector space models trained by a variety of algorithms (Word2vec: CBOW/skip-gram, fastText, or GloVe), and various word context choices such as linear word-order, syntax dependency grammars, and with and without knowledge of word position. These experiments show that our model, SVMCos, is robust to a range of experimental choices when training word embeddings.
摘要：我们与学习的话如何与在Word中嵌入空间相对彼此定位成新的方法进行实验。先前的方法学习常数向量偏移：矢量，从源点到令牌目标令牌与一个假设，即这些偏移量是彼此平行。我们表明，相关的标记之间的偏移量接近正交比并行，并且它们具有低余弦相似之处。我们继续通过使不同的假设;目标标记是由源和未标记的令牌线性可分。我们表明，一个最大利润的超平面可以分离目标令牌和该矢量正交的这个超平面表示源和目标之间的关系。我们发现，这种关系的这种表示在获得存款保险计划覆盖的语言规律的最好成绩。我们用由各种算法训练向量空间模型进行试验（Word2vec：CBOW /跳过克，fastText，或手套），以及各种单词的上下文的选择，如线性词序，语法依赖性文法，和有和没有字的知识位置。这些实验表明我们的模型，SVMCos，是稳健的一系列实验选择时的训练字的嵌入的。
11. Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory [PDF] 返回目录
Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart, Muhammad Bilal Sheikh
Abstract: State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation. In this work, we bring Affect Control Theory (ACT), a socio-mathematical model of emotions for human-human interactions, to the neural dialogue generation setting. ACT makes predictions about how humans respond to emotional stimuli in social situations. Due to this property, ACT and its derivative probabilistic models have been successfully deployed in several applications of Human-Computer Interaction, including empathetic tutoring systems, assistive healthcare devices and two-person social dilemma games. We investigate how ACT can be used to develop affect-aware conversational agents, which produce emotionally aligned responses to prompts and take into consideration the affective identities of the interactants.
摘要：国家的最先进的神经系统对话练成了语言的句法和语义建模，但往往也很难在对话期间建立与人类相互作用物的情感取向。在这项工作中，我们把影响控制理论（ACT），对人与人的互动情感的社会数学模型，对神经对话生成设置。 ACT使有关人类在社交场合的情绪刺激如何反应的预测。由于这种特性，ACT及其衍生概率模型已经成功部署在人机交互的多个应用程序，包括移情教学系统，辅助医疗设备和两个人的社会困境游戏。我们调查ACT如何被用来开发影响感知对话剂，它产生于提示情绪对准响应，并考虑到相互作用物的情感认同。
Nabiha Asghar, Ivan Kobyzev, Jesse Hoey, Pascal Poupart, Muhammad Bilal Sheikh
Abstract: State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation. In this work, we bring Affect Control Theory (ACT), a socio-mathematical model of emotions for human-human interactions, to the neural dialogue generation setting. ACT makes predictions about how humans respond to emotional stimuli in social situations. Due to this property, ACT and its derivative probabilistic models have been successfully deployed in several applications of Human-Computer Interaction, including empathetic tutoring systems, assistive healthcare devices and two-person social dilemma games. We investigate how ACT can be used to develop affect-aware conversational agents, which produce emotionally aligned responses to prompts and take into consideration the affective identities of the interactants.
摘要：国家的最先进的神经系统对话练成了语言的句法和语义建模，但往往也很难在对话期间建立与人类相互作用物的情感取向。在这项工作中，我们把影响控制理论（ACT），对人与人的互动情感的社会数学模型，对神经对话生成设置。 ACT使有关人类在社交场合的情绪刺激如何反应的预测。由于这种特性，ACT及其衍生概率模型已经成功部署在人机交互的多个应用程序，包括移情教学系统，辅助医疗设备和两个人的社会困境游戏。我们调查ACT如何被用来开发影响感知对话剂，它产生于提示情绪对准响应，并考虑到相互作用物的情感认同。
12. General-Purpose Communicative Function Recognition using a Hierarchical Network with Cascading Outputs and Maximum a Posteriori Path Estimation [PDF] 返回目录
Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
Abstract: ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions. The automatic recognition of these functions, although practically unexplored, is relevant for a dialog system, since they provide cues regarding the intention behind the segments and how they should be interpreted. In this paper, we explore the recognition of general-purpose communicative functions in the DialogBank, which is a reference set of dialogs annotated according to the standard. To do so, we adapt a state-of-the-art approach on flat dialog act recognition to deal with the hierarchical classification problem. More specifically, we propose the use of a hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is reduced, we rely both on additional dialogs annotated using mapping processes and on transfer learning to improve performance. The results of our experiments show that the hierarchical approach outperforms a flat one and that maximum a posteriori estimation outperforms an iterative prediction approach based on masking.
摘要：ISO 24617-2，为对话行为标注的标准，定义了分层组织一套通用的交际功能。自动识别这些功能，但几乎未开发的，是相关的对话系统，因为它们提供了关于段背后的意图以及应如何解释的线索。在本文中，我们探索的通用交际功能在DialogBank，这是一个参照组根据标准注释对话的识别。要做到这一点，我们适应在平坦的对话行为识别一个国家的最先进的方法来处理分层分类问题。更具体地讲，我们建议使用分层网络与级联输出和最大后验路径估计在每个层次预测交际功能，保存路径的功能之间的依赖关系，并决定在哪一个级别停止。此外，由于在DialogBank对话框的量减少时，我们依靠上都附加对话使用映射进程和转移学习提高性能注释。我们的实验结果表明，该分级方法比基于掩蔽平坦一个和最大的后验估计性能优于迭代预测方法。
Eugénio Ribeiro, Ricardo Ribeiro, David Martins de Matos
Abstract: ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions. The automatic recognition of these functions, although practically unexplored, is relevant for a dialog system, since they provide cues regarding the intention behind the segments and how they should be interpreted. In this paper, we explore the recognition of general-purpose communicative functions in the DialogBank, which is a reference set of dialogs annotated according to the standard. To do so, we adapt a state-of-the-art approach on flat dialog act recognition to deal with the hierarchical classification problem. More specifically, we propose the use of a hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is reduced, we rely both on additional dialogs annotated using mapping processes and on transfer learning to improve performance. The results of our experiments show that the hierarchical approach outperforms a flat one and that maximum a posteriori estimation outperforms an iterative prediction approach based on masking.
摘要：ISO 24617-2，为对话行为标注的标准，定义了分层组织一套通用的交际功能。自动识别这些功能，但几乎未开发的，是相关的对话系统，因为它们提供了关于段背后的意图以及应如何解释的线索。在本文中，我们探索的通用交际功能在DialogBank，这是一个参照组根据标准注释对话的识别。要做到这一点，我们适应在平坦的对话行为识别一个国家的最先进的方法来处理分层分类问题。更具体地讲，我们建议使用分层网络与级联输出和最大后验路径估计在每个层次预测交际功能，保存路径的功能之间的依赖关系，并决定在哪一个级别停止。此外，由于在DialogBank对话框的量减少时，我们依靠上都附加对话使用映射进程和转移学习提高性能注释。我们的实验结果表明，该分级方法比基于掩蔽平坦一个和最大的后验估计性能优于迭代预测方法。
13. ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification [PDF] 返回目录
Hongliang Bi, Pengyuan Liu
Abstract: Emotion cause analysis such as emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE) have gradually attracted the attention of many researchers. However, there are still two shortcomings in the existing research: 1) In most cases, emotion expression and cause are not the whole clause, but the span in the clause, so extracting the clause-pair rather than the span-pair greatly limits its applications in real-world scenarios; 2) It is not enough to extract the emotion expression clause without identifying the emotion categories, the presence of emotion clause does not necessarily convey emotional information explicitly due to different possible causes. In this paper, we propose a new task: Emotion-Cause Span-Pair extraction and classification (ECSP), which aims to extract the potential span-pair of emotion and corresponding causes in a document, and make emotion classification for each pair. In the new ECSP task, ECE and ECPE can be regarded as two special cases at the clause-level. We propose a span-based extract-then-classify (ETC) model, where emotion and cause are directly extracted and paired from the document under the supervision of target span boundaries, and corresponding categories are then classified using their pair representations and localized context. Experiments show that our proposed ETC model outperforms the SOTA model of ECE and ECPE task respectively and gets a fair-enough results on ECSP task.
摘要：情感原因分析，如情感原因提取（ECE）和情绪原因对的提取（ECPE）也逐渐吸引了众多研究者的注意。然而，仍然有在现有的研究有两个缺点：1）在大多数情况下，情感表达和事业是不是整个条款，但该条款的范围，所以提取子句对，而不是沿翼展对大大限制了它在真实世界场景的应用; 2）它是不够的提取情绪表达子句而不识别情感类别，情感条款的存在并不一定明确地由于不同的可能的原因传达情感信息。在本文中，我们提出了一个新的任务：情感原因跨度，对提取和分类（ECSP），其目的是提取潜在的跨度，对情感和文档中相应的原因，并进行情感分类的一对。在新的任务ECSP，欧洲经委会和ECPE可以看作是在条款级两种特殊情况。我们提出了一个基于整体范围的提取物，然后进行分类（ETC）模型，其中的情感和事业直接提取目标跨越边界的监督下从文件配对，然后相应类别使用的是他们对陈述和本地化方面进行分类。实验表明，我们提出的ETC模型分别优于ECE和ECPE任务的SOTA模型，得到在ECSP任务公平足够的结果。
Hongliang Bi, Pengyuan Liu
Abstract: Emotion cause analysis such as emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE) have gradually attracted the attention of many researchers. However, there are still two shortcomings in the existing research: 1) In most cases, emotion expression and cause are not the whole clause, but the span in the clause, so extracting the clause-pair rather than the span-pair greatly limits its applications in real-world scenarios; 2) It is not enough to extract the emotion expression clause without identifying the emotion categories, the presence of emotion clause does not necessarily convey emotional information explicitly due to different possible causes. In this paper, we propose a new task: Emotion-Cause Span-Pair extraction and classification (ECSP), which aims to extract the potential span-pair of emotion and corresponding causes in a document, and make emotion classification for each pair. In the new ECSP task, ECE and ECPE can be regarded as two special cases at the clause-level. We propose a span-based extract-then-classify (ETC) model, where emotion and cause are directly extracted and paired from the document under the supervision of target span boundaries, and corresponding categories are then classified using their pair representations and localized context. Experiments show that our proposed ETC model outperforms the SOTA model of ECE and ECPE task respectively and gets a fair-enough results on ECSP task.
摘要：情感原因分析，如情感原因提取（ECE）和情绪原因对的提取（ECPE）也逐渐吸引了众多研究者的注意。然而，仍然有在现有的研究有两个缺点：1）在大多数情况下，情感表达和事业是不是整个条款，但该条款的范围，所以提取子句对，而不是沿翼展对大大限制了它在真实世界场景的应用; 2）它是不够的提取情绪表达子句而不识别情感类别，情感条款的存在并不一定明确地由于不同的可能的原因传达情感信息。在本文中，我们提出了一个新的任务：情感原因跨度，对提取和分类（ECSP），其目的是提取潜在的跨度，对情感和文档中相应的原因，并进行情感分类的一对。在新的任务ECSP，欧洲经委会和ECPE可以看作是在条款级两种特殊情况。我们提出了一个基于整体范围的提取物，然后进行分类（ETC）模型，其中的情感和事业直接提取目标跨越边界的监督下从文件配对，然后相应类别使用的是他们对陈述和本地化方面进行分类。实验表明，我们提出的ETC模型分别优于ECE和ECPE任务的SOTA模型，得到在ECSP任务公平足够的结果。
14. A Post-processing Method for Detecting Unknown Intent of Dialogue System via Pre-trained Deep Neural Network Classifier [PDF] 返回目录
Ting-En Lin, Hua Xu
Abstract: With the maturity and popularity of dialogue systems, detecting user's unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks since we can hardly get examples, prior knowledge or the exact numbers of unknown intents. In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers. Our method can be flexibly applied on top of any classifiers trained in deep neural networks without changing the model architecture. We calibrate the confidence of the softmax outputs to compute the calibrated confidence score (i.e., SofterMax) and use it to calculate the decision boundary for unknown intent detection. Furthermore, we feed the feature representations learned by the deep neural networks into traditional novelty detection algorithm to detect unknown intents from different perspectives. Finally, we combine the methods above to perform the joint prediction. Our method classifies examples that differ from known intents as unknown and does not require any examples or prior knowledge of it. We have conducted extensive experiments on three benchmark dialogue datasets. The results show that our method can yield significant improvements compared with the state-of-the-art baselines
摘要：随着成熟度和对话系统的普及，检测对话系统用户的意图不明已成为一项重要任务。这也是最具挑战性的任务之一，因为我们很难得到例子，先验知识或不明意图的准确数字。在本文中，我们提出SofterMax和深新奇检测（SMDN），用于检测基于预训练深层神经网络分类对话系统未知的意图，一个简单而有效的后处理方法。我们的方法可以在深层神经网络训练的任何分类的顶部灵活运用不改变模型结构。我们校准SOFTMAX输出的信心来计算校准的信心评分（即SofterMax），并用它来计算未知意图检测决策边界。此外，我们通过饲料深层神经网络到传统的新颖性检测算法学会从不同的角度检测未知意图的特征表示。最后，我们结合上述方法进行联合预测。我们的方法进行分类的例子，从已知的意图不同，为未知，并且不需要任何实例或它的先验知识。我们对三个标准数据集的对话进行了广泛的实验。结果表明，我们的方法可以与国家的最先进的基线相比，产量显著改善
Ting-En Lin, Hua Xu
Abstract: With the maturity and popularity of dialogue systems, detecting user's unknown intent in dialogue systems has become an important task. It is also one of the most challenging tasks since we can hardly get examples, prior knowledge or the exact numbers of unknown intents. In this paper, we propose SofterMax and deep novelty detection (SMDN), a simple yet effective post-processing method for detecting unknown intent in dialogue systems based on pre-trained deep neural network classifiers. Our method can be flexibly applied on top of any classifiers trained in deep neural networks without changing the model architecture. We calibrate the confidence of the softmax outputs to compute the calibrated confidence score (i.e., SofterMax) and use it to calculate the decision boundary for unknown intent detection. Furthermore, we feed the feature representations learned by the deep neural networks into traditional novelty detection algorithm to detect unknown intents from different perspectives. Finally, we combine the methods above to perform the joint prediction. Our method classifies examples that differ from known intents as unknown and does not require any examples or prior knowledge of it. We have conducted extensive experiments on three benchmark dialogue datasets. The results show that our method can yield significant improvements compared with the state-of-the-art baselines
摘要：随着成熟度和对话系统的普及，检测对话系统用户的意图不明已成为一项重要任务。这也是最具挑战性的任务之一，因为我们很难得到例子，先验知识或不明意图的准确数字。在本文中，我们提出SofterMax和深新奇检测（SMDN），用于检测基于预训练深层神经网络分类对话系统未知的意图，一个简单而有效的后处理方法。我们的方法可以在深层神经网络训练的任何分类的顶部灵活运用不改变模型结构。我们校准SOFTMAX输出的信心来计算校准的信心评分（即SofterMax），并用它来计算未知意图检测决策边界。此外，我们通过饲料深层神经网络到传统的新颖性检测算法学会从不同的角度检测未知意图的特征表示。最后，我们结合上述方法进行联合预测。我们的方法进行分类的例子，从已知的意图不同，为未知，并且不需要任何实例或它的先验知识。我们对三个标准数据集的对话进行了广泛的实验。结果表明，我们的方法可以与国家的最先进的基线相比，产量显著改善
15. Synthetic Error Dataset Generation Mimicking Bengali Writing Pattern [PDF] 返回目录
Md. Habibur Rahman Sifat, Chowdhury Rafeed Rahman, Mohammad Rafsan, Md. Hasibur Rahman
Abstract: While writing Bengali using English keyboard, users often make spelling mistakes. The accuracy of any Bengali spell checker or paragraph correction module largely depends on the kind of error dataset it is based on. Manual generation of such error dataset is a cumbersome process. In this research, We present an algorithm for automatic misspelled Bengali word generation from correct word through analyzing Bengali writing pattern using QWERTY layout English keyboard. As part of our analysis, we have formed a list of most commonly used Bengali words, phonetically similar replaceable clusters, frequently mispressed replaceable clusters, frequently mispressed insertion prone clusters and some rules for Juktakkhar (constant letter clusters) handling while generating errors.
摘要：在使用英文键盘孟加拉语写作，用户经常会拼写错误。任何孟加拉语拼写检查或段落校正模块的精度在很大程度上取决于错误类型数据集它是基于。手动生成这样的误差数据集是一个麻烦的过程。在这项研究中，我们通过对使用QWERTY孟加拉语写作模式提出从正确的单词拼写错误自动孟加拉字生成算法布局英文键盘。作为我们分析的一部分，我们已形成的最常用的词孟加拉语，语音上相似的可更换集群，频繁更换mispressed集群，经常mispressed插入容易集群和Juktakkhar而产生的错误处理的一些规则（恒定字母集群）的列表。
Md. Habibur Rahman Sifat, Chowdhury Rafeed Rahman, Mohammad Rafsan, Md. Hasibur Rahman
Abstract: While writing Bengali using English keyboard, users often make spelling mistakes. The accuracy of any Bengali spell checker or paragraph correction module largely depends on the kind of error dataset it is based on. Manual generation of such error dataset is a cumbersome process. In this research, We present an algorithm for automatic misspelled Bengali word generation from correct word through analyzing Bengali writing pattern using QWERTY layout English keyboard. As part of our analysis, we have formed a list of most commonly used Bengali words, phonetically similar replaceable clusters, frequently mispressed replaceable clusters, frequently mispressed insertion prone clusters and some rules for Juktakkhar (constant letter clusters) handling while generating errors.
摘要：在使用英文键盘孟加拉语写作，用户经常会拼写错误。任何孟加拉语拼写检查或段落校正模块的精度在很大程度上取决于错误类型数据集它是基于。手动生成这样的误差数据集是一个麻烦的过程。在这项研究中，我们通过对使用QWERTY孟加拉语写作模式提出从正确的单词拼写错误自动孟加拉字生成算法布局英文键盘。作为我们分析的一部分，我们已形成的最常用的词孟加拉语，语音上相似的可更换集群，频繁更换mispressed集群，经常mispressed插入容易集群和Juktakkhar而产生的错误处理的一些规则（恒定字母集群）的列表。
16. Natural Language QA Approaches using Reasoning with External Knowledge [PDF] 返回目录
Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra
Abstract: Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's ``councilmen'' example in his 1972 paper and McCarthy's Mr. Hug example of 1976 highlights the role of external knowledge in NL understanding. While Machine Learning has been the go-to approach in NL processing as well as NL question answering (NLQA) for the last 30 years, recently there has been an increasingly emphasized thread on NLQA where external knowledge plays an important role. The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront. In this paper, we present a survey of the recent work on them. We believe our survey will help establish a bridge between multiple fields of AI, especially between (a) the traditional fields of knowledge representation and reasoning and (b) the field of NL understanding and NLQA.
摘要：答疑（QA）在自然语言（NL）已经AI从成立之初的一个重要方面。威诺格拉德的``议会议员'在他1972年纸例子，1976年的亮点外部知识的理解NL的作用麦卡锡的拥抱先生的例子。虽然机器学习已经去到在NL方法处理以及NL问答（NLQA）在过去的30年中，最近出现了对NLQA日益强调螺纹在外部知识起着重要的作用。通过威诺格拉德的议会议员榜样鼓舞的挑战，最近的事态发展，如重新引导AI书，各种NLQA数据集，就在NLQA背景知识获取的研究，以及它们在不同NLQA车型使用带来了NLQA的使用``推理'的问题与外部知识的最前沿。在本文中，我们提出对他们的近期工作进行了调查。我们相信，我们的调查将有助于建立AI的多个字段之间的桥梁，特别是（a）之间的知识表示和推理和（b）的NL理解和NLQA领域的传统领域。
Chitta Baral, Pratyay Banerjee, Kuntal Kumar Pal, Arindam Mitra
Abstract: Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's ``councilmen'' example in his 1972 paper and McCarthy's Mr. Hug example of 1976 highlights the role of external knowledge in NL understanding. While Machine Learning has been the go-to approach in NL processing as well as NL question answering (NLQA) for the last 30 years, recently there has been an increasingly emphasized thread on NLQA where external knowledge plays an important role. The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront. In this paper, we present a survey of the recent work on them. We believe our survey will help establish a bridge between multiple fields of AI, especially between (a) the traditional fields of knowledge representation and reasoning and (b) the field of NL understanding and NLQA.
摘要：答疑（QA）在自然语言（NL）已经AI从成立之初的一个重要方面。威诺格拉德的``议会议员'在他1972年纸例子，1976年的亮点外部知识的理解NL的作用麦卡锡的拥抱先生的例子。虽然机器学习已经去到在NL方法处理以及NL问答（NLQA）在过去的30年中，最近出现了对NLQA日益强调螺纹在外部知识起着重要的作用。通过威诺格拉德的议会议员榜样鼓舞的挑战，最近的事态发展，如重新引导AI书，各种NLQA数据集，就在NLQA背景知识获取的研究，以及它们在不同NLQA车型使用带来了NLQA的使用``推理'的问题与外部知识的最前沿。在本文中，我们提出对他们的近期工作进行了调查。我们相信，我们的调查将有助于建立AI的多个字段之间的桥梁，特别是（a）之间的知识表示和推理和（b）的NL理解和NLQA领域的传统领域。
17. NYTWIT: A Dataset of Novel Words in the New York Times [PDF] 返回目录
Yuval Pinter, Cassandra L. Jacobs, Max Bittker
Abstract: We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems. We hope this resource will prove useful for linguists and NLP practitioners by providing a real-world environment of novel word appearance.
摘要：我们提出了纽约时报的Word创新类型数据集或NYTWIT，2017年11月和2019年3月间发表在纽约时报的2500新英文单词的集合，手动注释为他们的类新奇的（如词汇推导，方言变化，共混，或配混）。我们本基线结果新颖类的既uncontextual和上下文预测，表明有改进的余地，即使对于国家的最先进的系统NLP。我们希望这一资源将通过提供新的词汇出现的一个真实世界的环境证明语言学家和NLP从业者非常有用。
Yuval Pinter, Cassandra L. Jacobs, Max Bittker
Abstract: We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding). We present baseline results for both uncontextual and contextual prediction of novelty class, showing that there is room for improvement even for state-of-the-art NLP systems. We hope this resource will prove useful for linguists and NLP practitioners by providing a real-world environment of novel word appearance.
摘要：我们提出了纽约时报的Word创新类型数据集或NYTWIT，2017年11月和2019年3月间发表在纽约时报的2500新英文单词的集合，手动注释为他们的类新奇的（如词汇推导，方言变化，共混，或配混）。我们本基线结果新颖类的既uncontextual和上下文预测，表明有改进的余地，即使对于国家的最先进的系统NLP。我们希望这一资源将通过提供新的词汇出现的一个真实世界的环境证明语言学家和NLP从业者非常有用。
18. TEDL: A Text Encryption Method Based on Deep Learning [PDF] 返回目录
Xiang Li, Peng Wang
Abstract: Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel text encryption method based on deep learning called TEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks while not degrade the communication efficiency.
摘要：近年来，人们对信息安全的日益重视，并且已经提出了多种加密方法。然而，对称加密方法，众所周知的加密技术仍然依赖于保证安全的关键空间和频繁的密钥更新受到影响。旨在解决这些问题，本文提出了一种基于所谓TEDL深度学习，那里的秘密密钥包括深度学习模型和加密的核心步骤是转换输入数据到下超参数训练的权重超参数一个小说文本加密方法。首先，通信双方通过根据指定超参数训练了深刻的学习模式建立的单词矢量表。然后，自更新码本构造上与SHA-256的功能和其他技巧词汇向量表。当通信开始，加密和解密是相当于索引和分别反转的码本索引，从而实现明文和密文之间的变换。实验和相关的分析结果表明，对于良好的安全性，效率，通用TEDL执行，并有重点的再分配的频率较低的需求。特别是，作为补充到当前加密方法，构建的码本的耗时的过程增加的蛮力攻击的难度，而不会降低通信效率。
Xiang Li, Peng Wang
Abstract: Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel text encryption method based on deep learning called TEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks while not degrade the communication efficiency.
摘要：近年来，人们对信息安全的日益重视，并且已经提出了多种加密方法。然而，对称加密方法，众所周知的加密技术仍然依赖于保证安全的关键空间和频繁的密钥更新受到影响。旨在解决这些问题，本文提出了一种基于所谓TEDL深度学习，那里的秘密密钥包括深度学习模型和加密的核心步骤是转换输入数据到下超参数训练的权重超参数一个小说文本加密方法。首先，通信双方通过根据指定超参数训练了深刻的学习模式建立的单词矢量表。然后，自更新码本构造上与SHA-256的功能和其他技巧词汇向量表。当通信开始，加密和解密是相当于索引和分别反转的码本索引，从而实现明文和密文之间的变换。实验和相关的分析结果表明，对于良好的安全性，效率，通用TEDL执行，并有重点的再分配的频率较低的需求。特别是，作为补充到当前加密方法，构建的码本的耗时的过程增加的蛮力攻击的难度，而不会降低通信效率。
19. Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA [PDF] 返回目录
Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Abstract: Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches.
摘要：知识图模型世界知识观念，实体和它们之间的关系，已被广泛应用于许多现实世界的任务。 CCKS 2019举行的评估与轨道6个任务，并吸引了超过1600支球队。在本文中，我们给出了在2019年CCKS知识图评价道的概述通过查看任务定义，每个任务CCKS 2019相关的成功的方法，有用的资源，良好的策略和研究的挑战，本文可以提供一个有益的参考开发知识图形应用程序，并进行未来的知识图研究。
Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Abstract: Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks. CCKS 2019 held an evaluation track with 6 tasks and attracted more than 1,600 teams. In this paper, we give an overview of the knowledge graph evaluation tract at CCKS 2019. By reviewing the task definition, successful methods, useful resources, good strategies and research challenges associated with each task in CCKS 2019, this paper can provide a helpful reference for developing knowledge graph applications and conducting future knowledge graph researches.
摘要：知识图模型世界知识观念，实体和它们之间的关系，已被广泛应用于许多现实世界的任务。 CCKS 2019举行的评估与轨道6个任务，并吸引了超过1600支球队。在本文中，我们给出了在2019年CCKS知识图评价道的概述通过查看任务定义，每个任务CCKS 2019相关的成功的方法，有用的资源，良好的策略和研究的挑战，本文可以提供一个有益的参考开发知识图形应用程序，并进行未来的知识图研究。
20. Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text [PDF] 返回目录
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
Abstract: There is inherent information captured in the order in which we write words in a list. The orderings of binomials --- lists of two words separated by `and' or `or' --- has been studied for more than a century. These binomials are common across many areas of speech, in both formal and informal text. In the last century, numerous explanations have been given to describe what order people use for these binomials, from differences in semantics to differences in phonology. These rules describe primarily `frozen' binomials that exist in exactly one ordering and have lacked large-scale trials to determine efficacy. Online text provides a unique opportunity to study these lists in the context of informal text at a very large scale. In this work, we expand the view of binomials to include a large-scale analysis of both frozen and non-frozen binomials in a quantitative way. Using this data, we then demonstrate that most previously proposed rules are ineffective at predicting binomial ordering. By tracking the order of these binomials across time and communities we are able to establish additional, unexplored dimensions central to these predictions. Expanding beyond the question of individual binomials, we also explore the global structure of binomials in various communities, establishing a new model for these lists and analyzing this structure for non-frozen and frozen binomials. Additionally, novel analysis of trinomials --- lists of length three --- suggests that none of the binomials analysis applies in these cases. Finally, we demonstrate how large data sets gleaned from the web can be used in conjunction with older theories to expand and improve on old questions.
摘要：是在我们的名单写单词的顺序拍摄的固有信息。二项式的排序---两个词列表分离用'和“或'或” ---已经研究了一个多世纪。这些二项式跨讲话的许多地区常见，在正式和非正式的文本。在上个世纪，众多的解释已获得来形容为了人们使用这些二项式，从语义音韵差异的差异。这些规则描述了存在于只有一个排序，并一直缺乏大规模临床试验疗效判断主要`冷冻”二项式。在线文本提供了一个独特的机会以一个非常大的规模来研究非正式文本的情况下，这些列表。在这项工作中，我们扩大二项式的视图，包括以定量方式既冷冻和非冷冻二项式的大规模分析。利用这些数据，我们则表明，大多数以前提出的规则都在预测二项式订货无效。通过跟踪跨越时间和社区这些二项式的订单，我们能够建立额外的，未开发的尺寸中央对这些预测。扩展超越个人二项式的问题，我们还探讨了二项式的全球结构中各个社区，建立这些列表的新模式，并分析了非冷冻和冷冻二项式这种结构。此外，三项式的新颖的分析---长度为三的名单---表明，没有一个二项式分析适用于这些情况。最后，我们将演示如何从大网络收集的数据集可以结合使用与旧理论来扩大和改善旧的问题。
Katherine Van Koevering, Austin R. Benson, Jon Kleinberg
Abstract: There is inherent information captured in the order in which we write words in a list. The orderings of binomials --- lists of two words separated by `and' or `or' --- has been studied for more than a century. These binomials are common across many areas of speech, in both formal and informal text. In the last century, numerous explanations have been given to describe what order people use for these binomials, from differences in semantics to differences in phonology. These rules describe primarily `frozen' binomials that exist in exactly one ordering and have lacked large-scale trials to determine efficacy. Online text provides a unique opportunity to study these lists in the context of informal text at a very large scale. In this work, we expand the view of binomials to include a large-scale analysis of both frozen and non-frozen binomials in a quantitative way. Using this data, we then demonstrate that most previously proposed rules are ineffective at predicting binomial ordering. By tracking the order of these binomials across time and communities we are able to establish additional, unexplored dimensions central to these predictions. Expanding beyond the question of individual binomials, we also explore the global structure of binomials in various communities, establishing a new model for these lists and analyzing this structure for non-frozen and frozen binomials. Additionally, novel analysis of trinomials --- lists of length three --- suggests that none of the binomials analysis applies in these cases. Finally, we demonstrate how large data sets gleaned from the web can be used in conjunction with older theories to expand and improve on old questions.
摘要：是在我们的名单写单词的顺序拍摄的固有信息。二项式的排序---两个词列表分离用'和“或'或” ---已经研究了一个多世纪。这些二项式跨讲话的许多地区常见，在正式和非正式的文本。在上个世纪，众多的解释已获得来形容为了人们使用这些二项式，从语义音韵差异的差异。这些规则描述了存在于只有一个排序，并一直缺乏大规模临床试验疗效判断主要`冷冻”二项式。在线文本提供了一个独特的机会以一个非常大的规模来研究非正式文本的情况下，这些列表。在这项工作中，我们扩大二项式的视图，包括以定量方式既冷冻和非冷冻二项式的大规模分析。利用这些数据，我们则表明，大多数以前提出的规则都在预测二项式订货无效。通过跟踪跨越时间和社区这些二项式的订单，我们能够建立额外的，未开发的尺寸中央对这些预测。扩展超越个人二项式的问题，我们还探讨了二项式的全球结构中各个社区，建立这些列表的新模式，并分析了非冷冻和冷冻二项式这种结构。此外，三项式的新颖的分析---长度为三的名单---表明，没有一个二项式分析适用于这些情况。最后，我们将演示如何从大网络收集的数据集可以结合使用与旧理论来扩大和改善旧的问题。
21. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective [PDF] 返回目录
Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi
Abstract: Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.
摘要：现在神经符号计算已经成为学术和行业研究实验室感兴趣的主题。图神经网络（GNN）已被广泛应用于关系和象征性的领域，在组合优化GNNS的广泛应用，约束满意度，关系推理等科学领域。对于需要改进explainability，解释性和AI系统的信任原则的方法，通过神经符号计算所建议的总体要求。在本文中，我们回顾了使用GNNS作为神经符号计算的模型中的国家的最先进的。这包括GNNS的几个结构域的应用程序，以及在神经符号计算其到目前的发展的关系。
Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar, Moshe Vardi
Abstract: Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.
摘要：现在神经符号计算已经成为学术和行业研究实验室感兴趣的主题。图神经网络（GNN）已被广泛应用于关系和象征性的领域，在组合优化GNNS的广泛应用，约束满意度，关系推理等科学领域。对于需要改进explainability，解释性和AI系统的信任原则的方法，通过神经符号计算所建议的总体要求。在本文中，我们回顾了使用GNNS作为神经符号计算的模型中的国家的最先进的。这包括GNNS的几个结构域的应用程序，以及在神经符号计算其到目前的发展的关系。
注：中文为机器翻译结果！