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2020.acl-main.573 | Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem . However , these memorybased methods usually suffer from overfitting the few memorized examples of old relations , which may gradually cause inevitable confusion among existing relations . Inspired by the mechanism in human long-term memory formation , we introduce episodic memory activation and reconsolidation ( EMAR ) to continual relation learning . Every time neural models are activated to learn both new and memorized data , EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations . The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models . The code and datasets are released on https://github.com / thunlp/ ContinualRE . Relation extraction aims at detecting relations between entities from text , e.g. , extracting the relation " the president of " from the given sentence " Newton served as the president of the Royal Society " , which could serve as external resource for various downstream applications ( Dong et al . , 2015 ; Xiong et al . , 2017 ; Schlichtkrull et al . , 2018 ) . The conventional RE methods ( Riedel et al . , 2013 ; Zeng et al . , 2014 ; Lin et al . , 2016 ) mostly focus on recognizing relations for a fixed pre-defined relation set , and can not handle rapidly emerging novel relations in the real world . Some researchers therefore explore to detect and learn incessantly emerging relations in an open scenario . As shown in Figure 1 , their efforts can be formulated into a two-step pipeline : ( 1 ) Open Relation Learning extracts phrases and arguments to construct patterns of specific relations , and then discovers unseen relation types by clustering patterns , and finally expands sufficient examples of new relation types from large-scale textual corpora ; ( 2 ) Continual Relation Learning continually uses those expanded examples of new relations to train an effective classifier . The classifier is trained on a sequence of tasks for handling both existing and novel relations , where each task has its own relation set . Although continual relation learning is vital for learning emerging relations , there are rare explorations for this field . A straightforward solution is to store all historical data and re-train models every time new relations and examples come in . Nevertheless , it is computationally expensive since relations are in sustainable growth . Moreover , the huge example number of each relation makes frequently mixing new and old examples become infeasible in the real world . Therefore , storing all data is not practical in continual relation learning . In view of this , the recent preliminary work ( Wang et al . , 2019 ) indicates that the main challenge of continual relation learning is the catastrophic forgetting problem , i.e. , it is hard to learn new relations and meanwhile avoid forgetting old relations , considering memorizing all the data is almost impossible . Recent work ( Shin et al . , 2017 ; Kemker and Kanan , 2018 ; Chaudhry et al . , 2019 ) has shown that the memory-based approaches , maintaining episodic memory to save a few training examples in old tasks and re-training memorized examples during training new tasks , are one of the most effective solutions to the catastrophic forgetting problem , especially for continual learning in NLP scenarios ( Wang et al . , 2019 ; d'Autume et al . , 2019 ) . However , existing memory-based models still suffer from an overfitting problem : when adapting them for continual relation learning , they may frequently change feature distribution of old relations , gradually overfit a few examples in memory , and finally become confused among old relations after long-term training . In fact , these memory-based methods are similar to long-term memory model of mammalian memory in neuroscience ( McClelland et al . , 1995 ; Bontempi et al . , 1999 ) . Although researchers in neuroscience are not clear about secrets inside the human brain , they reach a consensus that the formation of long-term memory relies on continually replaying and consolidating information ( Tononi and Cirelli , 2006 ; Boyce et al . , 2016 ; Yang et al . , 2014 ) , corresponding to the episodic memory and memory replay in continual learning models . Yet later work ( Nader et al . , 2000 ; Lee et al . , 2004 ; Alberini , 2005 ) in neuroscience indicates that reactivation of consolidated memory triggers a reconsolidation stage to continually maintain memory , and memory is easy to be changed or erased in this stage . To apply some reconsolidation exercises can help memory go through this stage and keep long-term memory stable . Intuitively , the ex-isting memory-based models seem like continual memory activation without reconsolidation exercises , and thus become sensitive and volatile . Inspired by the reconsolidation mechanism in human long-term memory formation , we introduce episodic memory activation and reconsolidation ( EMAR ) to continual relation learning in this paper . More specifically , when training models on new relations and their examples , we first adopt memory replay to activate neural models on examples of both new relations and memory , and then utilize a special reconsolidation module to let models avoid excessively changing and erasing feature distribution of old relations . As the core of relation learning is to grasp relation prototypes rather than rote memorization of relation examples , our reconsolidation module requires models to be able to distinguish old relation prototypes after each time memory is replayed and activated . As compared with pioneering explorations to improve episodic memory replay ( Chaudhry et al . , 2019 ; Wang et al . , 2019 ) , with toughly keeping feature distribution of old relations invariant , EMAR is more flexible in feature spaces and powerful in remembering relation prototypes . We conduct sufficient experiments on several RE datasets , and the results show that EMAR effectively alleviates the catastrophic forgetting problem and significantly outperforms the stateof-the-art continual learning models . Further experiments and analyses indicate the reasons for the effectiveness of EMAR , proving that it can utilize a few examples in old tasks to reconsolidate old relation prototypes and keep better distinction among old relations after long-term training . To alleviate catastrophically forgetting old relations in continual relation learning , we introduce episodic memory activation and reconsolidation ( EMAR ) , inspired by the mechanism in human long-term memory formation . Compared with existing memory-based methods , EMAR requires models to understand the prototypes of old relations rather than to overfit a few specific memorized examples , which can keep better distinction among relations after long-term training . We conduct experiments on three benchmarks in relation extraction and carry out extensive experimental results as well as empirical analyses , showing the effectiveness of EMAR on utilizing memorized examples . For future work , how to combine open relation learning and continual relation learning together to complete the pipeline for emerging relations still remains a problem , and we will continue to work on it . | Storing histories of examples is shown to be effective for continual relation learning however existing methods suffer from overfitting to memorize a few old memories. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
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Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent training is an effective solution for such a challenging problem . However , these memorybased methods usually suffer from overfitting the few memorized examples of old relations , which may gradually cause inevitable confusion among existing relations . Inspired by the mechanism in human long-term memory formation , we introduce episodic memory activation and reconsolidation ( EMAR ) to continual relation learning . Every time neural models are activated to learn both new and memorized data , EMAR utilizes relation prototypes for memory reconsolidation exercise to keep a stable understanding of old relations . The experimental results show that EMAR could get rid of catastrophically forgetting old relations and outperform the state-of-the-art continual learning models . The code and datasets are released on https://github.com / thunlp/ ContinualRE . Relation extraction aims at detecting relations between entities from text , e.g. , extracting the relation " the president of " from the given sentence " Newton served as the president of the Royal Society " , which could serve as external resource for various downstream applications ( Dong et al . , 2015 ; Xiong et al . , 2017 ; Schlichtkrull et al . , 2018 ) . The conventional RE methods ( Riedel et al . , 2013 ; Zeng et al . , 2014 ; Lin et al . , 2016 ) mostly focus on recognizing relations for a fixed pre-defined relation set , and can not handle rapidly emerging novel relations in the real world . Some researchers therefore explore to detect and learn incessantly emerging relations in an open scenario . As shown in Figure 1 , their efforts can be formulated into a two-step pipeline : ( 1 ) Open Relation Learning extracts phrases and arguments to construct patterns of specific relations , and then discovers unseen relation types by clustering patterns , and finally expands sufficient examples of new relation types from large-scale textual corpora ; ( 2 ) Continual Relation Learning continually uses those expanded examples of new relations to train an effective classifier . The classifier is trained on a sequence of tasks for handling both existing and novel relations , where each task has its own relation set . Although continual relation learning is vital for learning emerging relations , there are rare explorations for this field . A straightforward solution is to store all historical data and re-train models every time new relations and examples come in . Nevertheless , it is computationally expensive since relations are in sustainable growth . Moreover , the huge example number of each relation makes frequently mixing new and old examples become infeasible in the real world . Therefore , storing all data is not practical in continual relation learning . In view of this , the recent preliminary work ( Wang et al . , 2019 ) indicates that the main challenge of continual relation learning is the catastrophic forgetting problem , i.e. , it is hard to learn new relations and meanwhile avoid forgetting old relations , considering memorizing all the data is almost impossible . Recent work ( Shin et al . , 2017 ; Kemker and Kanan , 2018 ; Chaudhry et al . , 2019 ) has shown that the memory-based approaches , maintaining episodic memory to save a few training examples in old tasks and re-training memorized examples during training new tasks , are one of the most effective solutions to the catastrophic forgetting problem , especially for continual learning in NLP scenarios ( Wang et al . , 2019 ; d'Autume et al . , 2019 ) . However , existing memory-based models still suffer from an overfitting problem : when adapting them for continual relation learning , they may frequently change feature distribution of old relations , gradually overfit a few examples in memory , and finally become confused among old relations after long-term training . In fact , these memory-based methods are similar to long-term memory model of mammalian memory in neuroscience ( McClelland et al . , 1995 ; Bontempi et al . , 1999 ) . Although researchers in neuroscience are not clear about secrets inside the human brain , they reach a consensus that the formation of long-term memory relies on continually replaying and consolidating information ( Tononi and Cirelli , 2006 ; Boyce et al . , 2016 ; Yang et al . , 2014 ) , corresponding to the episodic memory and memory replay in continual learning models . Yet later work ( Nader et al . , 2000 ; Lee et al . , 2004 ; Alberini , 2005 ) in neuroscience indicates that reactivation of consolidated memory triggers a reconsolidation stage to continually maintain memory , and memory is easy to be changed or erased in this stage . To apply some reconsolidation exercises can help memory go through this stage and keep long-term memory stable . Intuitively , the ex-isting memory-based models seem like continual memory activation without reconsolidation exercises , and thus become sensitive and volatile . Inspired by the reconsolidation mechanism in human long-term memory formation , we introduce episodic memory activation and reconsolidation ( EMAR ) to continual relation learning in this paper . More specifically , when training models on new relations and their examples , we first adopt memory replay to activate neural models on examples of both new relations and memory , and then utilize a special reconsolidation module to let models avoid excessively changing and erasing feature distribution of old relations . As the core of relation learning is to grasp relation prototypes rather than rote memorization of relation examples , our reconsolidation module requires models to be able to distinguish old relation prototypes after each time memory is replayed and activated . As compared with pioneering explorations to improve episodic memory replay ( Chaudhry et al . , 2019 ; Wang et al . , 2019 ) , with toughly keeping feature distribution of old relations invariant , EMAR is more flexible in feature spaces and powerful in remembering relation prototypes . We conduct sufficient experiments on several RE datasets , and the results show that EMAR effectively alleviates the catastrophic forgetting problem and significantly outperforms the stateof-the-art continual learning models . Further experiments and analyses indicate the reasons for the effectiveness of EMAR , proving that it can utilize a few examples in old tasks to reconsolidate old relation prototypes and keep better distinction among old relations after long-term training . To alleviate catastrophically forgetting old relations in continual relation learning , we introduce episodic memory activation and reconsolidation ( EMAR ) , inspired by the mechanism in human long-term memory formation . Compared with existing memory-based methods , EMAR requires models to understand the prototypes of old relations rather than to overfit a few specific memorized examples , which can keep better distinction among relations after long-term training . We conduct experiments on three benchmarks in relation extraction and carry out extensive experimental results as well as empirical analyses , showing the effectiveness of EMAR on utilizing memorized examples . For future work , how to combine open relation learning and continual relation learning together to complete the pipeline for emerging relations still remains a problem , and we will continue to work on it .
| sobamchan/aclsum | 1 | full_paper |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social network provide useful information for classifying a user 's occupational group . In our study , we extend an existing dataset for this problem , and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work . In our analysis , we found that by using the graph convolutional network to exploit social homophily , we can achieve competitive performance on this dataset with just a small fraction of the training data . * Equal Contribution ; work performed while both authors were visiting Singapore University of Technology and Design ( SUTD ) . T1 : Day at the races . T2 : The new pitch era starts here . Groundsman . Wolves fan . Horse racing enthusiast . A Commercial and Domestic Grounds Maintenance company T1 : Pitch at wembley looks great . T2 : Spurs will have best stadium in uk . Twitter ( http://twitter.com ) is a microblogging service launched in 2006 , where , a user can publish messages with up to 280 characters , called " tweets " . Unlike many other social networking platforms , such as Facebook and LinkedIn , Twitter does not provide structured fields for users to fill in personal information . However , a user can write a 160-character-long small public summary about itself called a " Bio " . Besides linguistic information from tweets and Bios , online social media is a rich source of network information . People 's personal networks are homogeneous , i.e. , friends share more attributes such as race , ethnicity , religion , and occupation-known as the homophily principle ( McPherson et al . , 2001 ) . Such network information has been utilized in friend recommendation ( Guy et al . , 2010 ) , community detection Follow Figure 1 : User and Network information on Twitter Microblog . ( Yang and Leskovec , 2013 ) , etc . Figure 1 shows two users connected on Twitter . By looking at their Bio and tweets , it can be inferred that these users share the same occupational interest . Profiling users can enhance service quality and improve product recommendation , and hence is a widely studied problem . User occupational class prediction is an important component of user profiling and a sub-task of user demographic feature prediction . Existing approaches to predicting Twitter users ' demographic attributes explore , select , and combine various features generated from text and network to achieve the best predictive performances in respective classification tasks ( Han et al . , 2013 ; Miller et al . , 2012 ; Preot ¸iuc-Pietro et al . , 2015 ; Huang et al . , 2015 ; Aletras and Chamberlain , 2018 ) . The three categories of features are : account level features , tweet text features , and network based features . Past research have shown the distinctive usage of language across gender , age , location , etc . in tweets ( Sloan et al . , 2015 ; Cheng et al . , 2010 ; Burger et al . , 2011 ; Rao et al . , 2010 ) , which makes content based prediction effective . As for user occupational class prediction , Preot ¸iuc-Pietro et al . ( 2015 ) built a dataset where users are assigned to hierarchical job categories . They used word cluster distribution features of content information to predict a user 's occupational group . Aletras and Chamberlain ( 2018 ) constructed a user 's followings connections to learn the user embedding as a feature input to the classification models . Considering the regional disparities of economic development stages , the major job categories may vary significantly across regions . Sloan et al . ( 2015 ) summarized occupation distribution of Twitter users in the UK by looking into their profiles . In this paper , we analyze the usefulness of a user 's network information over the user 's tweets for predicting its occupational group . We extend the existing dataset for occupation classification ( Preot ¸iuc-Pietro et al . ( 2015 ) ) by introducing the network information about a user , i.e. follower / following IDs together with their Bio descriptions , and we construct a user-centric network to extract useful community and text based features . The acquired features from the network are then exploited using a graph neural network . The obtained results show the importance of a network information over tweet information from a user for such a task . Previous works have used tweets or a fraction of the network information to extract features for occupation classification . To analyze the importance of network information , we extended an existing Twitter dataset for a user 's social media connections ( follow information ) . We showed that by using only follow information as an input to graph convolutional networks , one can achieve a significantly higher accuracy on the prediction task as compared to the existing approaches utilizing tweet-only information or partial network structure . Directions of future research include adaptation of our methods to a large scale , sparsely connected social network . One might also want to investigate the inductive settings of GCN ( Hamilton et al . , 2017 ) to predict demographic information of a user from outside the black network . | Existing systems only use limited information from the tweets network to perform occupation classification. | challenge | coverage_first | State the difficulty of the study.
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In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social network provide useful information for classifying a user 's occupational group . In our study , we extend an existing dataset for this problem , and we achieve significantly better performance by using social network homophily that has not been fully exploited in previous work . In our analysis , we found that by using the graph convolutional network to exploit social homophily , we can achieve competitive performance on this dataset with just a small fraction of the training data . * Equal Contribution ; work performed while both authors were visiting Singapore University of Technology and Design ( SUTD ) . T1 : Day at the races . T2 : The new pitch era starts here . Groundsman . Wolves fan . Horse racing enthusiast . A Commercial and Domestic Grounds Maintenance company T1 : Pitch at wembley looks great . T2 : Spurs will have best stadium in uk . Twitter ( http://twitter.com ) is a microblogging service launched in 2006 , where , a user can publish messages with up to 280 characters , called " tweets " . Unlike many other social networking platforms , such as Facebook and LinkedIn , Twitter does not provide structured fields for users to fill in personal information . However , a user can write a 160-character-long small public summary about itself called a " Bio " . Besides linguistic information from tweets and Bios , online social media is a rich source of network information . People 's personal networks are homogeneous , i.e. , friends share more attributes such as race , ethnicity , religion , and occupation-known as the homophily principle ( McPherson et al . , 2001 ) . Such network information has been utilized in friend recommendation ( Guy et al . , 2010 ) , community detection Follow Figure 1 : User and Network information on Twitter Microblog . ( Yang and Leskovec , 2013 ) , etc . Figure 1 shows two users connected on Twitter . By looking at their Bio and tweets , it can be inferred that these users share the same occupational interest . Profiling users can enhance service quality and improve product recommendation , and hence is a widely studied problem . User occupational class prediction is an important component of user profiling and a sub-task of user demographic feature prediction . Existing approaches to predicting Twitter users ' demographic attributes explore , select , and combine various features generated from text and network to achieve the best predictive performances in respective classification tasks ( Han et al . , 2013 ; Miller et al . , 2012 ; Preot ¸iuc-Pietro et al . , 2015 ; Huang et al . , 2015 ; Aletras and Chamberlain , 2018 ) . The three categories of features are : account level features , tweet text features , and network based features . Past research have shown the distinctive usage of language across gender , age , location , etc . in tweets ( Sloan et al . , 2015 ; Cheng et al . , 2010 ; Burger et al . , 2011 ; Rao et al . , 2010 ) , which makes content based prediction effective . As for user occupational class prediction , Preot ¸iuc-Pietro et al . ( 2015 ) built a dataset where users are assigned to hierarchical job categories . They used word cluster distribution features of content information to predict a user 's occupational group . Aletras and Chamberlain ( 2018 ) constructed a user 's followings connections to learn the user embedding as a feature input to the classification models . Considering the regional disparities of economic development stages , the major job categories may vary significantly across regions . Sloan et al . ( 2015 ) summarized occupation distribution of Twitter users in the UK by looking into their profiles . In this paper , we analyze the usefulness of a user 's network information over the user 's tweets for predicting its occupational group . We extend the existing dataset for occupation classification ( Preot ¸iuc-Pietro et al . ( 2015 ) ) by introducing the network information about a user , i.e. follower / following IDs together with their Bio descriptions , and we construct a user-centric network to extract useful community and text based features . The acquired features from the network are then exploited using a graph neural network . The obtained results show the importance of a network information over tweet information from a user for such a task . Previous works have used tweets or a fraction of the network information to extract features for occupation classification . To analyze the importance of network information , we extended an existing Twitter dataset for a user 's social media connections ( follow information ) . We showed that by using only follow information as an input to graph convolutional networks , one can achieve a significantly higher accuracy on the prediction task as compared to the existing approaches utilizing tweet-only information or partial network structure . Directions of future research include adaptation of our methods to a large scale , sparsely connected social network . One might also want to investigate the inductive settings of GCN ( Hamilton et al . , 2017 ) to predict demographic information of a user from outside the black network .
| sobamchan/aclsum | 2 | full_paper |
2021.emnlp-main.185 | Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability . Conventional approaches employ the siamese-network for this task , which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-forward network on top of the sentence encoders . However , as the semantic textual similarity is commonly measured through the element-wise distance metrics ( e.g. cosine and L2 distance ) , such architecture yields a large gap between training and evaluating . In this paper , we propose DialogueCSE , a dialogue-based contrastive learning approach to tackle this issue . DialogueCSE first introduces a novel matching-guided embedding ( MGE ) mechanism , which generates a contextaware embedding for each candidate response embedding ( i.e. the context-free embedding ) according to the guidance of the multi-turn context-response matching matrices . Then it pairs each context-aware embedding with its corresponding context-free embedding and finally minimizes the contrastive loss across all pairs . We evaluate our model on three multi-turn dialogue datasets : the Microsoft Dialogue Corpus , the Jing Dong Dialogue Corpus , and the E-commerce Dialogue Corpus . Evaluation results show that our approach significantly outperforms the baselines across all three datasets in terms of MAP and Spearman 's correlation measures , demonstrating its effectiveness . Further quantitative experiments show that our approach achieves better performance when leveraging more dialogue context and remains robust when less training data is provided . Sentence embeddings are used with success for a variety of NLP applications ( Cer et al . , 2018 ) and many prior methods have been proposed with different learning schemes . Kiros et al . ( 2015 ) ; Logeswaran and Lee ( 2018 ) ; Hill et al . ( 2016 ) train sentence encoders in a self-supervised manner with web pages and books . Conneau et al . ( 2017 ) ; Cer et al . ( 2018 ) ; Reimers and Gurevych ( 2019 ) propose to learn sentence embeddings on the supervised datasets such as SNLI ( Bowman et al . , 2015 ) and MNLI ( Williams et al . , 2018 ) . Although the supervised-learning approaches achieve better performance , they suffer from high cost of annotation in building the training dataset , which makes them hard to adapt to other domains or languages . Recently , learning sentence embeddings from dialogues has begun to attract increasing attention . Dialogues provide strong semantic relationships among conversational utterances and are usually easy to collect in large amounts . Such advantages make the dialogue-based self-supervised learning methods promising to achieve competitive or even superior performance against the supervised-learning methods , especially under the low-resource conditions . While promising , the issue of how to effectively exploit the dialogues for this task has not been sufficiently explored . Yang et al . ( 2018 ) propose to train an input-response prediction model on Reddit dataset ( Al-Rfou et al . , 2016 ) . Since they build their architecture based on the single-turn dialogue , the multi-turn dialogue history is not fully exploited . Henderson et al . ( 2020 ) demonstrate that introducing the multi-turn dialogue context can improve the sentence embedding performance . However , they concatenate the multi-turn dialogue context into a long token sequence , failing to model intersentence semantic relationships among the utterances . Recently , more advanced methods such as ( Reimers and Gurevych , 2019 ) achieve better performance by employing BERT ( Devlin et al . , 2019 ) as the sentence encoder . These works have in common that they employ a feed-forward network with a non-linear activation on top of the sentence en-coders to model the context-response semantic relevance , thereby learning the sentence embeddings . However , such architecture presents two limitations : ( 1 ) It yields a large gap between training and evaluating , since the semantic textual similarity is commonly measured by the element-wise distance metrics such as cosine and L2 distance . ( 2 ) Concatenating all the utterances in the dialogue context inevitably introduces the noise as well as the redundant information , resulting in a poor result . In this paper , we propose DialogueCSE , a dialogue-based contrastive learning approach to tackle these issues . We hold that the semantic matching relationships between the context and the response can be implicitly modeled through contrastive learning , thus making it possible to eliminate the gap between training and evaluating . To this end , we introduce a novel matching-guided embedding ( MGE ) mechanism . Specifically , MGE first pairs each utterance in the context with the response and performs a token-level dot-product operation across all the utterance-response pairs to obtain the multi-turn matching matrices . Then the multi-turn matching matrices are used as guidance to generate a context-aware embedding for the response embedding ( i.e. the context-free embedding ) . Finally , the context-aware embedding and the context-free embedding are paired as a training sample , whose label is determined by whether the context and the response are originally from the same dialogue . Our motivation is that once the context semantically matches the response , it has the ability to distill the context-aware information from the context-free embedding , which is exactly the learning objective of the sentence encoder that aims to produce context-aware sentence embeddings . We train our model on three multi-turn dialogue datasets : the Microsoft Dialogue Corpus ( MDC ) ( Li et al . , 2018 ) , the Jing Dong Dialogue Corpus ( JDDC ) ( Chen et al . , 2020 ) , and the E-commerce Dialogue Corpus ( ECD ) ( Zhang et al . , 2018 ) . To evaluate our model , we introduce two types of tasks : the semantic retrieval ( SR ) task and the dialogue-based semantic textual similarity ( D-STS ) task . Here we do not adopt the standard semantic textual similarity ( STS ) task ( Cer et al . , 2017 ) for two reasons : ( 1 ) As revealed in ( Zhang et al . , 2020 ) , the sentence embedding performance varies greatly as the domain of the training data changes . As a dialogue dataset is always about several certain domains , evaluating on the STS benchmark may mis-lead the evaluation of the model . ( 2 ) The dialoguebased sentence embeddings focus on context-aware rather than context-free semantic meanings , which may not be suitable to be evaluated through the context-free benchmarks . Since previous dialoguebased works have not set up a uniform benchmark , we construct two evaluation datasets for each dialogue corpus . A total of 18,964 retrieval samples and 4,000 sentence pairs are annotated by seven native speakers through the crowd-sourcing platform1 . The evaluation results indicate that DialogueCSE significantly outperforms the baselines on the three datasets in terms of both MAP and Spearman 's correlation metrics , demonstrating its effectiveness . Further quantitative experiments show that Dia-logueCSE achieves better performance when leveraging more dialogue context and remains robust when less training data is provided . To sum up , our contributions are threefold : • We propose DialogueCSE , a dialogue-based contrastive learning approach with MGE mechanism for learning sentence embeddings from dialogues . As far as we know , this is the first attempt to apply contrastive learning in this area . • We construct the dialogue-based sentence embedding evaluation benchmarks for three dialogue corpus . All of the datasets will be released to facilitate the follow-up researches . • Extensive experiments show that Dia-logueCSE significantly outperforms the baselines , establishing the state-of-the-art results . 2 Related Work In this work , we propose DialogueCSE , a dialoguebased contrastive learning approach to learn sentence embeddings from dialogues . We also propose uniform evaluation benchmarks for evaluating the quality of the dialogue-based sentence embeddings . Evaluation results show that DialogueCSE achieves the best result over the baselines while adding no additional parameters . In the next step , we will study how to introduce more interaction information to learn the sentence embeddings and try to incorporate the contrast learning method into the pre-training stage . | They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues by modelling semantic matching relationships between the context and response implicitly. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
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Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability . Conventional approaches employ the siamese-network for this task , which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-forward network on top of the sentence encoders . However , as the semantic textual similarity is commonly measured through the element-wise distance metrics ( e.g. cosine and L2 distance ) , such architecture yields a large gap between training and evaluating . In this paper , we propose DialogueCSE , a dialogue-based contrastive learning approach to tackle this issue . DialogueCSE first introduces a novel matching-guided embedding ( MGE ) mechanism , which generates a contextaware embedding for each candidate response embedding ( i.e. the context-free embedding ) according to the guidance of the multi-turn context-response matching matrices . Then it pairs each context-aware embedding with its corresponding context-free embedding and finally minimizes the contrastive loss across all pairs . We evaluate our model on three multi-turn dialogue datasets : the Microsoft Dialogue Corpus , the Jing Dong Dialogue Corpus , and the E-commerce Dialogue Corpus . Evaluation results show that our approach significantly outperforms the baselines across all three datasets in terms of MAP and Spearman 's correlation measures , demonstrating its effectiveness . Further quantitative experiments show that our approach achieves better performance when leveraging more dialogue context and remains robust when less training data is provided . Sentence embeddings are used with success for a variety of NLP applications ( Cer et al . , 2018 ) and many prior methods have been proposed with different learning schemes . Kiros et al . ( 2015 ) ; Logeswaran and Lee ( 2018 ) ; Hill et al . ( 2016 ) train sentence encoders in a self-supervised manner with web pages and books . Conneau et al . ( 2017 ) ; Cer et al . ( 2018 ) ; Reimers and Gurevych ( 2019 ) propose to learn sentence embeddings on the supervised datasets such as SNLI ( Bowman et al . , 2015 ) and MNLI ( Williams et al . , 2018 ) . Although the supervised-learning approaches achieve better performance , they suffer from high cost of annotation in building the training dataset , which makes them hard to adapt to other domains or languages . Recently , learning sentence embeddings from dialogues has begun to attract increasing attention . Dialogues provide strong semantic relationships among conversational utterances and are usually easy to collect in large amounts . Such advantages make the dialogue-based self-supervised learning methods promising to achieve competitive or even superior performance against the supervised-learning methods , especially under the low-resource conditions . While promising , the issue of how to effectively exploit the dialogues for this task has not been sufficiently explored . Yang et al . ( 2018 ) propose to train an input-response prediction model on Reddit dataset ( Al-Rfou et al . , 2016 ) . Since they build their architecture based on the single-turn dialogue , the multi-turn dialogue history is not fully exploited . Henderson et al . ( 2020 ) demonstrate that introducing the multi-turn dialogue context can improve the sentence embedding performance . However , they concatenate the multi-turn dialogue context into a long token sequence , failing to model intersentence semantic relationships among the utterances . Recently , more advanced methods such as ( Reimers and Gurevych , 2019 ) achieve better performance by employing BERT ( Devlin et al . , 2019 ) as the sentence encoder . These works have in common that they employ a feed-forward network with a non-linear activation on top of the sentence en-coders to model the context-response semantic relevance , thereby learning the sentence embeddings . However , such architecture presents two limitations : ( 1 ) It yields a large gap between training and evaluating , since the semantic textual similarity is commonly measured by the element-wise distance metrics such as cosine and L2 distance . ( 2 ) Concatenating all the utterances in the dialogue context inevitably introduces the noise as well as the redundant information , resulting in a poor result . In this paper , we propose DialogueCSE , a dialogue-based contrastive learning approach to tackle these issues . We hold that the semantic matching relationships between the context and the response can be implicitly modeled through contrastive learning , thus making it possible to eliminate the gap between training and evaluating . To this end , we introduce a novel matching-guided embedding ( MGE ) mechanism . Specifically , MGE first pairs each utterance in the context with the response and performs a token-level dot-product operation across all the utterance-response pairs to obtain the multi-turn matching matrices . Then the multi-turn matching matrices are used as guidance to generate a context-aware embedding for the response embedding ( i.e. the context-free embedding ) . Finally , the context-aware embedding and the context-free embedding are paired as a training sample , whose label is determined by whether the context and the response are originally from the same dialogue . Our motivation is that once the context semantically matches the response , it has the ability to distill the context-aware information from the context-free embedding , which is exactly the learning objective of the sentence encoder that aims to produce context-aware sentence embeddings . We train our model on three multi-turn dialogue datasets : the Microsoft Dialogue Corpus ( MDC ) ( Li et al . , 2018 ) , the Jing Dong Dialogue Corpus ( JDDC ) ( Chen et al . , 2020 ) , and the E-commerce Dialogue Corpus ( ECD ) ( Zhang et al . , 2018 ) . To evaluate our model , we introduce two types of tasks : the semantic retrieval ( SR ) task and the dialogue-based semantic textual similarity ( D-STS ) task . Here we do not adopt the standard semantic textual similarity ( STS ) task ( Cer et al . , 2017 ) for two reasons : ( 1 ) As revealed in ( Zhang et al . , 2020 ) , the sentence embedding performance varies greatly as the domain of the training data changes . As a dialogue dataset is always about several certain domains , evaluating on the STS benchmark may mis-lead the evaluation of the model . ( 2 ) The dialoguebased sentence embeddings focus on context-aware rather than context-free semantic meanings , which may not be suitable to be evaluated through the context-free benchmarks . Since previous dialoguebased works have not set up a uniform benchmark , we construct two evaluation datasets for each dialogue corpus . A total of 18,964 retrieval samples and 4,000 sentence pairs are annotated by seven native speakers through the crowd-sourcing platform1 . The evaluation results indicate that DialogueCSE significantly outperforms the baselines on the three datasets in terms of both MAP and Spearman 's correlation metrics , demonstrating its effectiveness . Further quantitative experiments show that Dia-logueCSE achieves better performance when leveraging more dialogue context and remains robust when less training data is provided . To sum up , our contributions are threefold : • We propose DialogueCSE , a dialogue-based contrastive learning approach with MGE mechanism for learning sentence embeddings from dialogues . As far as we know , this is the first attempt to apply contrastive learning in this area . • We construct the dialogue-based sentence embedding evaluation benchmarks for three dialogue corpus . All of the datasets will be released to facilitate the follow-up researches . • Extensive experiments show that Dia-logueCSE significantly outperforms the baselines , establishing the state-of-the-art results . 2 Related Work In this work , we propose DialogueCSE , a dialoguebased contrastive learning approach to learn sentence embeddings from dialogues . We also propose uniform evaluation benchmarks for evaluating the quality of the dialogue-based sentence embeddings . Evaluation results show that DialogueCSE achieves the best result over the baselines while adding no additional parameters . In the next step , we will study how to introduce more interaction information to learn the sentence embeddings and try to incorporate the contrast learning method into the pre-training stage .
| sobamchan/aclsum | 2 | full_paper |
2021.naacl-main.72 | Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models . Attention redundancy has been observed among attention heads but has not been deeply studied in the literature . Using BERT-base model as an example , this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression . We analyze the attention redundancy with Five-Ws and How . ( What ) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets . ( How ) We use both token-based and sentence-based distance functions to measure the redundancy . ( Where ) Clear and similar redundancy patterns ( cluster structure ) are observed among attention heads . ( When ) Redundancy patterns are similar in both pre-training and fine-tuning phases . ( Who ) We discover that redundancy patterns are task-agnostic . Similar redundancy patterns even exist for randomly generated token sequences . ( " Why " ) We also evaluate influences of the pre-training dropout ratios on attention redundancy . Based on the phaseindependent and task-agnostic attention redundancy patterns , we propose a simple zero-shot pruning method as a case study . Experiments on fine-tuning GLUE tasks verify its effectiveness . The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising . Multi-layer multi-head self-attention architectures ( Transformer ( Vaswani et al . , 2017 ) ) are widely applied in modern language models , such as BERT ( Devlin et al . , 2019 ) , RoBERTa ( Liu et al . , 2019 ) , OpenAI GPT ( Radford et al . , 2018 ) , GPT-2 ( Radford et al . , 2019 ) and ERNIE2.0 ( Sun et al . , 2019 ) , to name a few . Redundancy phenomenon is discovered among attention heads . It demonstrates that many attention heads generate very similar attention matrices ( Clark et al . , 2019 ; Kovaleva et al . , 2019 ) . We take the pre-trained BERT-base model as an example . It learns 12-layer-12-head self-attention matrices describing dependencies between each pair of tokens in a sentence . Then for each token , there are 144 attention vectors . We use Jensen-Shannon distance to measure the relationship between each pair of vectors . Then for one sentence ( consisting of a sequence of tokens ) , the token-averaged distance is utilized to imply the redundancy between each pair of attention matrices . Smaller distance values reflect more redundancy . Figure 1 shows the redundancy ( distance ) among 144 × 144 pairs of attention matrices averaged over 1000 randomly sampled sentences . We can see clear redundancy patterns ( clusters with smaller distance areas ) in consecutive attention layers . Analyzing the attention redundancy helps to interpret the multi-layer multi-head self-attention architecture . Various studies have attempted to re-veal the relationship among attention heads . Examples are attention visualization ( Vig and Belinkov , 2019 ) , common attention patterns ( Kovaleva et al . , 2019 ) , attention head pruning ( Voita et al . , 2019 ) , and probing test ( Clark et al . , 2019 ) . Existing works either focus on the 12 × 12 attention matrices and their effects on ( pre-training or / and finetuning ) performances or focus on linguistic features extracted by latent token vectors and attention matrices . Though the redundancy phenomenon was discovered , no existing work studies the attention redundancy pattern itself ( i.e. , the 144 × 144 distance matrix in Figure 1 ) deeply . This motivates us to conduct a comprehensive and complementary study on the attention redundancy phenomenon . In this paper , we take the BERT-base model as a representative model to analyze the attention redundancy with Five Ws and How . As far as we know , many of the following discoveries are new to the research community . What is attention redundancy ? Given a distance function , we define the pairwise distance matrix ( ∈ R 144×144 ) of the 12 × 12 attention matrices of BERT-base model as attention redundancy matrix . In this paper , we obtain redundancy matrices from both pre-trained and finetuned BERT-base model for GLUE tasks as the research objects . How to measure attention redundancy ? Except for the two token-based measures , Jensen-Shannon distance ( Clark et al . , 2019 ) and cosine similarity ( Kovaleva et al . , 2019 ) used in literature , we employ two more token-based distance function and three sentence-based ones to measure attention redundancy and analyze their similar redundancy patterns ( please refer to Section 4.1 for more details ) . The purpose is to alleviate the measuring bias of just using one distance function . Sentence-based distances directly measure the relationship between two attention matrices without averaging over tokens . We visualize the redundancy patterns using various distance functions . Where does attention redundancy exist ? We find common hierarchical cluster structures in the set of token-based redundancy matrices and the set of sentence-based redundancy matrices , respectively . Attention heads of earlier , middle , and deeper attention layers are clearly clustered in the redundancy matrices . We also demonstrate that highly correlated similar redundancy patterns exist in redundancy matrices generated based on different type of distances . When does attention redundancy occur ? The redundancy is phase-independent . Common redundancy patterns are discovered in both the pre-trained phase and fine-tuned phases . For any downstream task with any distance function , we notice highly correlated attention redundancy patterns between two phases . Who ( which task ) has attention redundancy ? We surprisingly realize that the redundancy is task-agnostic . The redundancy patterns are highly correlated across different tasks . We even randomly generate token sequences as input in the pre-trained BERT-base model . Very similar attention redundancy patterns occur as well . Based on this astonishing discovery , as a case study application , we propose a simple zero-shot head-pruning strategy based on clustering results using redundancy matrices . Compared to other complex pruning strategies , e.g. , ( Tang et al . , 2019 ; Jiao et al . , 2019 ; Fan et al . , 2019 ; Wang et al . , 2019 ; McCarley , 2019 ) , the most important is that without knowing any data of fine-tuning tasks , this pruning can be effectively and efficiently conducted just based on some randomly generated token sequences with the pre-trained BERT-base model . The only effort is to compute one or several redundancy matrices . Results reflect that for most GLUE tasks , the proposed pruning strategy based on redundancy matrices can prune up to 75 % to 85 % of attention heads while keeping comparable fine-tuning performances . " Why " does the phase-independent and taskagnostic attention redundancy happen ? It 's hard to tell the reason of the redundancy patterns ( that 's why we use the quoted " Why " ) . However , we conduct experiments to evaluate the effects on attention redundancy of dropout ratios in the pre-training phase which are suspected as one of reasons ( Clark et al . , 2019 ) . When we use sentence-based distance , a monotonic trend is found . Attention heads tend to be more redundant when increasing dropout ratios . When we use token-based distances , a complex " N"-shape effect exists . We also notice that the redundancy is more sensitive to dropouts in hidden linear transformations than to dropouts in the self-attention mechanism . We believe that above-mentioned new findings in this paper make the redundancy analyses a promis-ing research direction in model interpretation and model compression , and so on . Using BERT-base model as an example , we comprehensively investigated the attention redundancy in multi-layer multi-head self-attention based language models . The redundancy was measured by distance functions at token level and sentence level . At both levels , we found that many heads are not distinct from each other , and clear clustering effects were observed . We discovered that the attention redundancy is phase-independent and taskagnostic . Specifically , compared to a pre-trained model , the redundancy patterns do not change much after fine-tuning on multiple downstream tasks . We also shown complex influences on redundancy of dropout ratios in hidden transformations and self-attention . Based on these discoveries , we design a zero-shot strategy to prune attention heads . Compared to existing methods , the zero-shot pruning is simple and robust ( task-agnostic ) . In the near future , we are interested in experimenting this method over more self-attention based pre-trained language models and more downstream tasks . This is the appendix for NAACL-HLT 2021 paper : Yuchen Bian , Jiaji Huang , Xingyu Cai , Jiahong Yuan , and Kenneth Church . On Attention Redundancy : A Comprehensive Study . | They perform token and sentence level analysis on redundancy matrices from pre-trained and fine-tuned BERT-base models and further propose a pruning method based on findings. | approach | coverage_first | Give the approach described.
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Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models . Attention redundancy has been observed among attention heads but has not been deeply studied in the literature . Using BERT-base model as an example , this paper provides a comprehensive study on attention redundancy which is helpful for model interpretation and model compression . We analyze the attention redundancy with Five-Ws and How . ( What ) We define and focus the study on redundancy matrices generated from pre-trained and fine-tuned BERT-base model for GLUE datasets . ( How ) We use both token-based and sentence-based distance functions to measure the redundancy . ( Where ) Clear and similar redundancy patterns ( cluster structure ) are observed among attention heads . ( When ) Redundancy patterns are similar in both pre-training and fine-tuning phases . ( Who ) We discover that redundancy patterns are task-agnostic . Similar redundancy patterns even exist for randomly generated token sequences . ( " Why " ) We also evaluate influences of the pre-training dropout ratios on attention redundancy . Based on the phaseindependent and task-agnostic attention redundancy patterns , we propose a simple zero-shot pruning method as a case study . Experiments on fine-tuning GLUE tasks verify its effectiveness . The comprehensive analyses on attention redundancy make model understanding and zero-shot model pruning promising . Multi-layer multi-head self-attention architectures ( Transformer ( Vaswani et al . , 2017 ) ) are widely applied in modern language models , such as BERT ( Devlin et al . , 2019 ) , RoBERTa ( Liu et al . , 2019 ) , OpenAI GPT ( Radford et al . , 2018 ) , GPT-2 ( Radford et al . , 2019 ) and ERNIE2.0 ( Sun et al . , 2019 ) , to name a few . Redundancy phenomenon is discovered among attention heads . It demonstrates that many attention heads generate very similar attention matrices ( Clark et al . , 2019 ; Kovaleva et al . , 2019 ) . We take the pre-trained BERT-base model as an example . It learns 12-layer-12-head self-attention matrices describing dependencies between each pair of tokens in a sentence . Then for each token , there are 144 attention vectors . We use Jensen-Shannon distance to measure the relationship between each pair of vectors . Then for one sentence ( consisting of a sequence of tokens ) , the token-averaged distance is utilized to imply the redundancy between each pair of attention matrices . Smaller distance values reflect more redundancy . Figure 1 shows the redundancy ( distance ) among 144 × 144 pairs of attention matrices averaged over 1000 randomly sampled sentences . We can see clear redundancy patterns ( clusters with smaller distance areas ) in consecutive attention layers . Analyzing the attention redundancy helps to interpret the multi-layer multi-head self-attention architecture . Various studies have attempted to re-veal the relationship among attention heads . Examples are attention visualization ( Vig and Belinkov , 2019 ) , common attention patterns ( Kovaleva et al . , 2019 ) , attention head pruning ( Voita et al . , 2019 ) , and probing test ( Clark et al . , 2019 ) . Existing works either focus on the 12 × 12 attention matrices and their effects on ( pre-training or / and finetuning ) performances or focus on linguistic features extracted by latent token vectors and attention matrices . Though the redundancy phenomenon was discovered , no existing work studies the attention redundancy pattern itself ( i.e. , the 144 × 144 distance matrix in Figure 1 ) deeply . This motivates us to conduct a comprehensive and complementary study on the attention redundancy phenomenon . In this paper , we take the BERT-base model as a representative model to analyze the attention redundancy with Five Ws and How . As far as we know , many of the following discoveries are new to the research community . What is attention redundancy ? Given a distance function , we define the pairwise distance matrix ( ∈ R 144×144 ) of the 12 × 12 attention matrices of BERT-base model as attention redundancy matrix . In this paper , we obtain redundancy matrices from both pre-trained and finetuned BERT-base model for GLUE tasks as the research objects . How to measure attention redundancy ? Except for the two token-based measures , Jensen-Shannon distance ( Clark et al . , 2019 ) and cosine similarity ( Kovaleva et al . , 2019 ) used in literature , we employ two more token-based distance function and three sentence-based ones to measure attention redundancy and analyze their similar redundancy patterns ( please refer to Section 4.1 for more details ) . The purpose is to alleviate the measuring bias of just using one distance function . Sentence-based distances directly measure the relationship between two attention matrices without averaging over tokens . We visualize the redundancy patterns using various distance functions . Where does attention redundancy exist ? We find common hierarchical cluster structures in the set of token-based redundancy matrices and the set of sentence-based redundancy matrices , respectively . Attention heads of earlier , middle , and deeper attention layers are clearly clustered in the redundancy matrices . We also demonstrate that highly correlated similar redundancy patterns exist in redundancy matrices generated based on different type of distances . When does attention redundancy occur ? The redundancy is phase-independent . Common redundancy patterns are discovered in both the pre-trained phase and fine-tuned phases . For any downstream task with any distance function , we notice highly correlated attention redundancy patterns between two phases . Who ( which task ) has attention redundancy ? We surprisingly realize that the redundancy is task-agnostic . The redundancy patterns are highly correlated across different tasks . We even randomly generate token sequences as input in the pre-trained BERT-base model . Very similar attention redundancy patterns occur as well . Based on this astonishing discovery , as a case study application , we propose a simple zero-shot head-pruning strategy based on clustering results using redundancy matrices . Compared to other complex pruning strategies , e.g. , ( Tang et al . , 2019 ; Jiao et al . , 2019 ; Fan et al . , 2019 ; Wang et al . , 2019 ; McCarley , 2019 ) , the most important is that without knowing any data of fine-tuning tasks , this pruning can be effectively and efficiently conducted just based on some randomly generated token sequences with the pre-trained BERT-base model . The only effort is to compute one or several redundancy matrices . Results reflect that for most GLUE tasks , the proposed pruning strategy based on redundancy matrices can prune up to 75 % to 85 % of attention heads while keeping comparable fine-tuning performances . " Why " does the phase-independent and taskagnostic attention redundancy happen ? It 's hard to tell the reason of the redundancy patterns ( that 's why we use the quoted " Why " ) . However , we conduct experiments to evaluate the effects on attention redundancy of dropout ratios in the pre-training phase which are suspected as one of reasons ( Clark et al . , 2019 ) . When we use sentence-based distance , a monotonic trend is found . Attention heads tend to be more redundant when increasing dropout ratios . When we use token-based distances , a complex " N"-shape effect exists . We also notice that the redundancy is more sensitive to dropouts in hidden linear transformations than to dropouts in the self-attention mechanism . We believe that above-mentioned new findings in this paper make the redundancy analyses a promis-ing research direction in model interpretation and model compression , and so on . Using BERT-base model as an example , we comprehensively investigated the attention redundancy in multi-layer multi-head self-attention based language models . The redundancy was measured by distance functions at token level and sentence level . At both levels , we found that many heads are not distinct from each other , and clear clustering effects were observed . We discovered that the attention redundancy is phase-independent and taskagnostic . Specifically , compared to a pre-trained model , the redundancy patterns do not change much after fine-tuning on multiple downstream tasks . We also shown complex influences on redundancy of dropout ratios in hidden transformations and self-attention . Based on these discoveries , we design a zero-shot strategy to prune attention heads . Compared to existing methods , the zero-shot pruning is simple and robust ( task-agnostic ) . In the near future , we are interested in experimenting this method over more self-attention based pre-trained language models and more downstream tasks . This is the appendix for NAACL-HLT 2021 paper : Yuchen Bian , Jiaji Huang , Xingyu Cai , Jiahong Yuan , and Kenneth Church . On Attention Redundancy : A Comprehensive Study .
| sobamchan/aclsum | 1 | full_paper |
N13-1083 | We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acoustic and language model scores for detecting filled pauses through constrained speech recognition . We compare the contributions of different knowledge sources to detection performance between these two languages . Speech disfluencies are common phenomena in spontaneous speech . They consist of spoken words and phrases that represent self-correction , hesitation , and floor-grabbing behaviors , but do not add semantic information ; removing them yields the intended , fluent utterance . The presence of disfluencies in conversational speech data can cause problems for both downstream processing ( parsing and other natural language processing tasks ) and human readability of speech transcripts . There has been much research effort on automatic disfluency detection in recent years ( Shriberg and Stolcke , 1997 ; Snover et al . , 2004 ; Liu et al . , 2006 ; Lin and Lee , 2009 ; Schuler et al . , 2010 ; Georgila et al . , 2010 ; Zwarts and Johnson , 2011 ) , particularly from the DARPA EARS ( Effective , Affordable , Reusable Speech-to-Text ) MDE ( MetaData Extraction ) ( DARPA Information Processing Technology Office , 2003 ) program , which focused on the automatic transcription of sizable amounts of speech data and rendering such transcripts in readable form , for both conversational telephone speech ( CTS ) and broadcast news ( BN ) . However , the EARS MDE effort was focused on English only , and there has n't been much research on the effectiveness of similar automatic disfluency detection approaches for multiple languages . This paper presents three main innovations . First , we extend the EARS MDE-style disfluency detection approach combining lexical and prosodic features using a Conditional Random Field ( CRF ) model , which was employed for detecting disfluency on English conversational speech data ( Liu et al . , 2005 ) , to Mandarin conversational speech , as presented in Section 2 . Second , we implement an automatic filled pause detection approach through constrained speech recognition , as presented in Section 3 . Third , for both disfluency detection systems , we compare side-by-side contributions of different knowledge sources to detection performance for two languages , English and Mandarin , as presented in Section 4 . Conclusions appear in Section 5 . In conclusion , we have presented two automatic disfluency detection systems , one combining various lexical and prosodic features , and the other combining LVCSR acoustic and language model knowledge sources . We observed significant improvements in combining lexical and prosodic features over just employing word n-gram features , for both languages . When combining AM and LM knowledge sources for FP detection in constrained speech recognition , we found increasing LM weight improved both false alarm and miss rates for Mandarin but degraded the miss rate for English . | They evaluate a Conditional Random Field-based edit disfluency detection model and a system which combines acoustic and language model that detects filled pauses in Mandarin. | approach | coverage_first | Summarize the method.
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We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acoustic and language model scores for detecting filled pauses through constrained speech recognition . We compare the contributions of different knowledge sources to detection performance between these two languages . Speech disfluencies are common phenomena in spontaneous speech . They consist of spoken words and phrases that represent self-correction , hesitation , and floor-grabbing behaviors , but do not add semantic information ; removing them yields the intended , fluent utterance . The presence of disfluencies in conversational speech data can cause problems for both downstream processing ( parsing and other natural language processing tasks ) and human readability of speech transcripts . There has been much research effort on automatic disfluency detection in recent years ( Shriberg and Stolcke , 1997 ; Snover et al . , 2004 ; Liu et al . , 2006 ; Lin and Lee , 2009 ; Schuler et al . , 2010 ; Georgila et al . , 2010 ; Zwarts and Johnson , 2011 ) , particularly from the DARPA EARS ( Effective , Affordable , Reusable Speech-to-Text ) MDE ( MetaData Extraction ) ( DARPA Information Processing Technology Office , 2003 ) program , which focused on the automatic transcription of sizable amounts of speech data and rendering such transcripts in readable form , for both conversational telephone speech ( CTS ) and broadcast news ( BN ) . However , the EARS MDE effort was focused on English only , and there has n't been much research on the effectiveness of similar automatic disfluency detection approaches for multiple languages . This paper presents three main innovations . First , we extend the EARS MDE-style disfluency detection approach combining lexical and prosodic features using a Conditional Random Field ( CRF ) model , which was employed for detecting disfluency on English conversational speech data ( Liu et al . , 2005 ) , to Mandarin conversational speech , as presented in Section 2 . Second , we implement an automatic filled pause detection approach through constrained speech recognition , as presented in Section 3 . Third , for both disfluency detection systems , we compare side-by-side contributions of different knowledge sources to detection performance for two languages , English and Mandarin , as presented in Section 4 . Conclusions appear in Section 5 . In conclusion , we have presented two automatic disfluency detection systems , one combining various lexical and prosodic features , and the other combining LVCSR acoustic and language model knowledge sources . We observed significant improvements in combining lexical and prosodic features over just employing word n-gram features , for both languages . When combining AM and LM knowledge sources for FP detection in constrained speech recognition , we found increasing LM weight improved both false alarm and miss rates for Mandarin but degraded the miss rate for English .
| sobamchan/aclsum | 0 | full_paper |
2021.naacl-main.150 | A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition ( ASR ) and neural machine translation ( NMT ) tasks . However , since the input modalities are different , it is difficult to leverage source language text successfully . In this work , we focus on sequencelevel knowledge distillation ( SeqKD ) from external text-based NMT models . To leverage the full potential of the source language information , we propose backward SeqKD , SeqKD from a target-to-source backward NMT model . To this end , we train a bilingual E2E-ST model to predict paraphrased transcriptions as an auxiliary task with a single decoder . The paraphrases are generated from the translations in bitext via back-translation . We further propose bidirectional SeqKD in which SeqKD from both forward and backward NMT models is combined . Experimental evaluations on both autoregressive and non-autoregressive models show that SeqKD in each direction consistently improves the translation performance , and the effectiveness is complementary regardless of the model capacity . End-to-end speech translation ( E2E-ST ) ( Bérard et al . , 2016 ) , which aims to convert source speech to text in another language directly , is an active research area . Because direct ST is a more difficult task than automatic speech recognition ( ASR ) and machine translation ( MT ) , various techniques have been proposed to ease the training process by using source transcription . Examples include pretraining ( Bérard et al . , 2018 ; Wang et al . , 2020c ; Bansal et al . , 2019 ; Wang et al . , 2020d ) , multi-task learning ( Weiss et al . , 2017 ; Bérard et al . , 2018 ; Bahar et al . , 2019 ) , knowledge distillation ( Liu et al . , 2019 ) , meta-learning ( Indurthi et al . , 2020 ) , twopass decoding ( Anastasopoulos and Chiang , 2018 ; Sperber et al . , 2019 ) , and interactive decoding ( Liu et al . , 2020 ; Le et al . , 2020 ) . However , as input modalities between ST and MT tasks are different , an auxiliary MT task is not always helpful , especially when additional bitext is not available ( Bahar et al . , 2019 ) . Moreover , because monotonic speech-to-transcription alignments encourage the ASR task to see surface-level local information , an auxiliary ASR task helps the E2E-ST model to extract acoustic representations , not semantic ones , from speech . Sequence-level knowledge distillation ( Se-qKD ) ( Kim and Rush , 2016 ) is another approach to transferring knowledge from one model to another . Recent studies have shown that SeqKD has the effect of reducing the complexity of training data and thus eases the training of student models , e.g. , non-autoregressive ( NAR ) models ( Gu et al . , 2018 ; Zhou et al . , 2019a ; Ren et al . , 2020 ) . Paraphrasing , which represents text in a different form but with the same meaning , can also be regarded as SeqKD when using neural paraphrasing via back-translation ( Mallinson et al . , 2017 ; Wieting et al . , 2017 ; Federmann et al . , 2019 ) . It has been studied to improve the reference diversity for MT system evaluations ( Thompson and Post , 2020 ; Bawden et al . , 2020a , b ) and the performance of low-resource neural MT ( NMT ) models ( Zhou et al . , 2019b ; Khayrallah et al . , 2020 ) . In this work , due to its simplicity and effectiveness , we focus on SeqKD from text-based NMT models to improve the performance of a bilingual E2E-ST model . In order to fully leverage source language information , we propose backward Se-qKD , which targets paraphrased source transcriptions generated from a target-to-source backward NMT model as an auxiliary task . Then , a single ST decoder is trained to predict both source and target language text as in a multilingual setting ( Inaguma et al . , 2019 ) . This way , the decoder is biased to capture semantic representations from speech , un-like joint training with an auxiliary ASR task . We also propose bidirectional SeqKD , which combines SeqKD from two NMT models in both language directions . Therefore , the E2E-ST models can fully exploit the knowledge embedded in both forward and backward NMT models . Experimental evaluations demonstrate that Se-qKD from each direction consistently improves the translation performance of both autoregressive and non-autoregressive E2E-ST models . We also confirm that bidirectional SeqKD outperforms unidirectional SeqKD and that the effectiveness is maintained in large models . To fully leverage knowledge in both source and target language directions for bilingual E2E-ST models , we have proposed bidirectional SeqKD , in which both forward SeqKD from a source-to-target NMT model and backward SeqKD from a target-tosource NMT model are combined . Backward Se-qKD is performed by targeting source paraphrases generated via back-translation from the original translations in bitext . Then , the E2E-ST model is enhanced by training to generate both source and target language text with a single decoder . We experimentally confirmed that SeqKD from each direction boosted the translation performance of both autoregressive and non-autoregressive E2E-ST models , and the effectiveness was additive . Multi-referenced training with the original and distilled text gave further gains . We also showed that bidirectional SeqKD was effective regardless of model sizes . | Evaluations on autoregressive and non-autoregressive models show that the proposed method improves in both directions and the results are consistent in different model sizes. | outcome | aspect_first | What are the primary outcomes of the study? Give a concise overview. Return only the summary in one sentence.
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A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition ( ASR ) and neural machine translation ( NMT ) tasks . However , since the input modalities are different , it is difficult to leverage source language text successfully . In this work , we focus on sequencelevel knowledge distillation ( SeqKD ) from external text-based NMT models . To leverage the full potential of the source language information , we propose backward SeqKD , SeqKD from a target-to-source backward NMT model . To this end , we train a bilingual E2E-ST model to predict paraphrased transcriptions as an auxiliary task with a single decoder . The paraphrases are generated from the translations in bitext via back-translation . We further propose bidirectional SeqKD in which SeqKD from both forward and backward NMT models is combined . Experimental evaluations on both autoregressive and non-autoregressive models show that SeqKD in each direction consistently improves the translation performance , and the effectiveness is complementary regardless of the model capacity . End-to-end speech translation ( E2E-ST ) ( Bérard et al . , 2016 ) , which aims to convert source speech to text in another language directly , is an active research area . Because direct ST is a more difficult task than automatic speech recognition ( ASR ) and machine translation ( MT ) , various techniques have been proposed to ease the training process by using source transcription . Examples include pretraining ( Bérard et al . , 2018 ; Wang et al . , 2020c ; Bansal et al . , 2019 ; Wang et al . , 2020d ) , multi-task learning ( Weiss et al . , 2017 ; Bérard et al . , 2018 ; Bahar et al . , 2019 ) , knowledge distillation ( Liu et al . , 2019 ) , meta-learning ( Indurthi et al . , 2020 ) , twopass decoding ( Anastasopoulos and Chiang , 2018 ; Sperber et al . , 2019 ) , and interactive decoding ( Liu et al . , 2020 ; Le et al . , 2020 ) . However , as input modalities between ST and MT tasks are different , an auxiliary MT task is not always helpful , especially when additional bitext is not available ( Bahar et al . , 2019 ) . Moreover , because monotonic speech-to-transcription alignments encourage the ASR task to see surface-level local information , an auxiliary ASR task helps the E2E-ST model to extract acoustic representations , not semantic ones , from speech . Sequence-level knowledge distillation ( Se-qKD ) ( Kim and Rush , 2016 ) is another approach to transferring knowledge from one model to another . Recent studies have shown that SeqKD has the effect of reducing the complexity of training data and thus eases the training of student models , e.g. , non-autoregressive ( NAR ) models ( Gu et al . , 2018 ; Zhou et al . , 2019a ; Ren et al . , 2020 ) . Paraphrasing , which represents text in a different form but with the same meaning , can also be regarded as SeqKD when using neural paraphrasing via back-translation ( Mallinson et al . , 2017 ; Wieting et al . , 2017 ; Federmann et al . , 2019 ) . It has been studied to improve the reference diversity for MT system evaluations ( Thompson and Post , 2020 ; Bawden et al . , 2020a , b ) and the performance of low-resource neural MT ( NMT ) models ( Zhou et al . , 2019b ; Khayrallah et al . , 2020 ) . In this work , due to its simplicity and effectiveness , we focus on SeqKD from text-based NMT models to improve the performance of a bilingual E2E-ST model . In order to fully leverage source language information , we propose backward Se-qKD , which targets paraphrased source transcriptions generated from a target-to-source backward NMT model as an auxiliary task . Then , a single ST decoder is trained to predict both source and target language text as in a multilingual setting ( Inaguma et al . , 2019 ) . This way , the decoder is biased to capture semantic representations from speech , un-like joint training with an auxiliary ASR task . We also propose bidirectional SeqKD , which combines SeqKD from two NMT models in both language directions . Therefore , the E2E-ST models can fully exploit the knowledge embedded in both forward and backward NMT models . Experimental evaluations demonstrate that Se-qKD from each direction consistently improves the translation performance of both autoregressive and non-autoregressive E2E-ST models . We also confirm that bidirectional SeqKD outperforms unidirectional SeqKD and that the effectiveness is maintained in large models . To fully leverage knowledge in both source and target language directions for bilingual E2E-ST models , we have proposed bidirectional SeqKD , in which both forward SeqKD from a source-to-target NMT model and backward SeqKD from a target-tosource NMT model are combined . Backward Se-qKD is performed by targeting source paraphrases generated via back-translation from the original translations in bitext . Then , the E2E-ST model is enhanced by training to generate both source and target language text with a single decoder . We experimentally confirmed that SeqKD from each direction boosted the translation performance of both autoregressive and non-autoregressive E2E-ST models , and the effectiveness was additive . Multi-referenced training with the original and distilled text gave further gains . We also showed that bidirectional SeqKD was effective regardless of model sizes .
| sobamchan/aclsum | 1 | full_paper |
2021.acl-long.420 | We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa . Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage , so that they suffer from discrepancy between the two stages . Such a problem would lead to the necessity of having human-annotated syntactic information , which limits the application of existing methods to broader scenarios . To address this , we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages . Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text . We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree . We evaluate the model on three downstream tasks , including relation classification , entity typing , and question answering . Results show that our model achieves state-of-the-art performance on six public benchmark datasets . We have two major findings . First , we demonstrate that infusing automatically produced syntax of text improves pre-trained models . Second , global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens . 1 * Work is done during internship at Microsoft . † For questions , please contact D. Tang and Z. Xu . Pre-trained models such as BERT ( Devlin et al . , 2019 ) , GPT ( Radford et al . , 2018 ) , and RoBERTa ( Liu et al . , 2019 ) have advanced the state-of-the-art performances of various natural language processing tasks . The successful recipe is that a model is first pre-trained on a huge volume of unsupervised data with self-supervised objectives , and then is fine-tuned on supervised data with the same data scheme . Dominant pre-trained models represent a text as a sequence of tokens2 . The merits are that such basic text representations are available from vast amounts of unsupervised data , and that models pre-trained and fine-tuned with the same paradigm usually achieve good accuracy in practice ( Guu et al . , 2020 ) . However , an evident limitation of these methods is that richer syntactic structure of text is ignored . In this paper , we seek to enhance pre-trained models with syntax of text . Related studies attempt to inject syntax information either only in the finetuning stage ( Nguyen et al . , 2020 ; Sachan et al . , 2020 ) , or only in the pre-training stage ( Wang et al . , 2020 ) , which results in discrepancies . When only fusing syntax information in the fine-tuning phase , Sachan et al . ( 2020 ) finds that there is no performance boost unless high quality human-annotated dependency parses are available . However , this requirement would limit the application of the model to broader scenarios where human-annotated dependency information is not available . To address this , we conduct a large-scale study on injecting automatically produced syntax of text in both the pre-training and fine-tuning stages . We construct a pre-training dataset by applying an offthe-shelf dependency parser ( Qi et al . , 2020 ) to one billion sentences from common crawl news . With these data , we introduce a syntax-aware pretraining task , called dependency distance prediction , which predicts the syntactic distance between tokens in the dependency structure . Compared with the pre-training task of dependency head prediction ( Wang et al . , 2020 ) that only captures local syntactic relations among words , dependency distance prediction leverages global syntax of the text . In addition , we developed a syntax-aware attention layer , which can be conveniently integrated into Transformer ( Vaswani et al . , 2017 ) to allow tokens to selectively attend to contextual tokens based on their syntactic distance in the dependency structure . We conduct experiments on entity typing , question answering and relation classification on six benchmark datasets . Experimental results show that our method achieves state-of-the-art performance on all six datasets . Further analysis shows that our model can indicate the importance of syntactic information on downstream tasks , and that the newly introduced dependency distance prediction task could capture the global syntax of the text , performs better than dependency head prediction . In addition , compared with experimental results of injecting syntax information in either the pre-training or fine-tuning stage , injecting syntax information in both stages achieves the best performance . In summary , the contribution of this paper is threefold . ( 1 ) We demonstrate that infusing automatically produced dependency structures into the pre-trained model shows superior performance over downstream tasks . ( 2 ) We propose a syntax-aware attention layer and a pre-training task for infusing syntactic information into the pre-trained model . ( 3 ) We find that the newly introduced dependency distance prediction task performs better than the dependency head prediction task . In this paper , we present SEPREM that leverage syntax information to enhance pre-trained models . To inject syntactic information , we introduce a syntax-aware attention layer and a newly designed pre-training task are proposed . Experimental results show that our method achieves state-of-theart performance over six datasets . Further analysis shows that the proposed dependency distance prediction task performs better than dependency head prediction task . | Existing ways of injecting syntactic knowledge into pretraining models cause discrepancies between pretraining and fine-tuning and require expensive annotation. | challenge | coverage_first | Give the problem in short form.
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We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa . Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage , so that they suffer from discrepancy between the two stages . Such a problem would lead to the necessity of having human-annotated syntactic information , which limits the application of existing methods to broader scenarios . To address this , we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages . Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text . We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree . We evaluate the model on three downstream tasks , including relation classification , entity typing , and question answering . Results show that our model achieves state-of-the-art performance on six public benchmark datasets . We have two major findings . First , we demonstrate that infusing automatically produced syntax of text improves pre-trained models . Second , global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens . 1 * Work is done during internship at Microsoft . † For questions , please contact D. Tang and Z. Xu . Pre-trained models such as BERT ( Devlin et al . , 2019 ) , GPT ( Radford et al . , 2018 ) , and RoBERTa ( Liu et al . , 2019 ) have advanced the state-of-the-art performances of various natural language processing tasks . The successful recipe is that a model is first pre-trained on a huge volume of unsupervised data with self-supervised objectives , and then is fine-tuned on supervised data with the same data scheme . Dominant pre-trained models represent a text as a sequence of tokens2 . The merits are that such basic text representations are available from vast amounts of unsupervised data , and that models pre-trained and fine-tuned with the same paradigm usually achieve good accuracy in practice ( Guu et al . , 2020 ) . However , an evident limitation of these methods is that richer syntactic structure of text is ignored . In this paper , we seek to enhance pre-trained models with syntax of text . Related studies attempt to inject syntax information either only in the finetuning stage ( Nguyen et al . , 2020 ; Sachan et al . , 2020 ) , or only in the pre-training stage ( Wang et al . , 2020 ) , which results in discrepancies . When only fusing syntax information in the fine-tuning phase , Sachan et al . ( 2020 ) finds that there is no performance boost unless high quality human-annotated dependency parses are available . However , this requirement would limit the application of the model to broader scenarios where human-annotated dependency information is not available . To address this , we conduct a large-scale study on injecting automatically produced syntax of text in both the pre-training and fine-tuning stages . We construct a pre-training dataset by applying an offthe-shelf dependency parser ( Qi et al . , 2020 ) to one billion sentences from common crawl news . With these data , we introduce a syntax-aware pretraining task , called dependency distance prediction , which predicts the syntactic distance between tokens in the dependency structure . Compared with the pre-training task of dependency head prediction ( Wang et al . , 2020 ) that only captures local syntactic relations among words , dependency distance prediction leverages global syntax of the text . In addition , we developed a syntax-aware attention layer , which can be conveniently integrated into Transformer ( Vaswani et al . , 2017 ) to allow tokens to selectively attend to contextual tokens based on their syntactic distance in the dependency structure . We conduct experiments on entity typing , question answering and relation classification on six benchmark datasets . Experimental results show that our method achieves state-of-the-art performance on all six datasets . Further analysis shows that our model can indicate the importance of syntactic information on downstream tasks , and that the newly introduced dependency distance prediction task could capture the global syntax of the text , performs better than dependency head prediction . In addition , compared with experimental results of injecting syntax information in either the pre-training or fine-tuning stage , injecting syntax information in both stages achieves the best performance . In summary , the contribution of this paper is threefold . ( 1 ) We demonstrate that infusing automatically produced dependency structures into the pre-trained model shows superior performance over downstream tasks . ( 2 ) We propose a syntax-aware attention layer and a pre-training task for infusing syntactic information into the pre-trained model . ( 3 ) We find that the newly introduced dependency distance prediction task performs better than the dependency head prediction task . In this paper , we present SEPREM that leverage syntax information to enhance pre-trained models . To inject syntactic information , we introduce a syntax-aware attention layer and a newly designed pre-training task are proposed . Experimental results show that our method achieves state-of-theart performance over six datasets . Further analysis shows that the proposed dependency distance prediction task performs better than dependency head prediction task .
| sobamchan/aclsum | 1 | full_paper |
P18-1222 | Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general embedding approach for hyper-documents , namely , hyperdoc2vec , along with four criteria characterizing necessary information that hyper-document embedding models should preserve . Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks , i.e. , paper classification and citation recommendation , in the academic paper domain . Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t . the four criteria . The ubiquitous World Wide Web has boosted research interests on hypertext documents , e.g. , personal webpages ( Lu and Getoor , 2003 ) , Wikipedia pages ( Gabrilovich and Markovitch , 2007 ) , as well as academic papers ( Sugiyama and Kan , 2010 ) . Unlike independent plain documents , a hypertext document ( hyper-doc for short ) links to another hyper-doc by a hyperlink or citation mark in its textual content . Given this essential distinction , hyperlinks or citations are worth specific modeling in many tasks such as link-based classification ( Lu and Getoor , 2003 ) , web retrieval ( Page et al . , 1999 ) , entity linking ( Cucerzan , 2007 ) , and citation recommendation ( He et al . , 2010 ) . To model hypertext documents , various efforts ( Cohn and Hofmann , 2000 ; Kataria et al . , 2010 ; Perozzi et al . , 2014 ; Zwicklbauer et al . , 2016 ; Wang et al . , 2016 ) have been made to depict networks of hyper-docs as well as their content . Among potential techniques , distributed representation ( Mikolov et al . , 2013 ; Le and Mikolov , 2014 ) tends to be promising since its validity and effectiveness are proven for plain documents on many natural language processing ( NLP ) tasks . Conventional attempts on utilizing embedding techniques in hyper-doc-related tasks generally fall into two types . The first type ( Berger et al . , 2017 ; Zwicklbauer et al . , 2016 ) simply downcasts hyper-docs to plain documents and feeds them into word2vec ( Mikolov et al . , 2013 ) ( w2v for short ) or doc2vec ( Le and Mikolov , 2014 ) ( d2v for short ) . These approaches involve downgrading hyperlinks and inevitably omit certain information in hyper-docs . However , no previous work investigates the information loss , and how it affects the performance of such downcasting-based adaptations . The second type designs sophisticated embedding models to fulfill certain tasks , e.g. , citation recommendation ( Huang et al . , 2015b ) , paper classification ( Wang et al . , 2016 ) , and entity linking ( Yamada et al . , 2016 ) , etc . These models are limited to specific tasks , and it is yet unknown whether embeddings learned for those particular tasks can generalize to others . Based on the above facts , we are interested in two questions : • What information should hyper-doc embedding models preserve , and what nice property should they possess ? • Is there a general approach to learning taskindependent embeddings of hyper-docs ? To answer the two questions , we formalize the hyper-doc embedding task , and propose four criteria , i.e. , content awareness , context awareness , newcomer friendliness , and context intent aware-ness , to assess different models . Then we discuss simple downcasting-based adaptations of existing approaches w.r.t . the above criteria , and demonstrate that none of them satisfy all four . To this end , we propose hyperdoc2vec ( h-d2v for short ) , a general embedding approach for hyperdocs . Different from most existing approaches , h-d2v learns two vectors for each hyper-doc to characterize its roles of citing others and being cited . Owning to this , h-d2v is able to directly model hyperlinks or citations without downgrading them . To evaluate the learned embeddings , we employ two tasks in the academic paper domain1 , i.e. , paper classification and citation recommendation . Experimental results demonstrate the superiority of h-d2v . Comparative studies and controlled experiments also confirm that h-d2v benefits from satisfying the above four criteria . We summarize our contributions as follows : • We propose four criteria to assess different hyper-document embedding models . • We propose hyperdoc2vec , a general embedding approach for hyper-documents . • We systematically conduct comparisons with competing approaches , validating the superiority of h-d2v in terms of the four criteria . We focus on the hyper-doc embedding problem . We propose that hyper-doc embedding algorithms should be content aware , context aware , newcomer friendly , and context intent aware . To meet all four criteria , we propose a general approach , hyperdoc2vec , which assigns two vectors to each hyper-doc and models citations in a straightforward manner . In doing so , the learned embeddings satisfy all criteria , which no existing model is able to . For evaluation , paper classification and citation recommendation are conducted on three academic paper datasets . Results confirm the effectiveness of our approach . Further analyses also demonstrate that possessing the four properties helps h-d2v outperform other models . | Existing text embedding methods do not take structures of hyper-documents into account losing useful properties for downstream tasks. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
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Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general embedding approach for hyper-documents , namely , hyperdoc2vec , along with four criteria characterizing necessary information that hyper-document embedding models should preserve . Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks , i.e. , paper classification and citation recommendation , in the academic paper domain . Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t . the four criteria . The ubiquitous World Wide Web has boosted research interests on hypertext documents , e.g. , personal webpages ( Lu and Getoor , 2003 ) , Wikipedia pages ( Gabrilovich and Markovitch , 2007 ) , as well as academic papers ( Sugiyama and Kan , 2010 ) . Unlike independent plain documents , a hypertext document ( hyper-doc for short ) links to another hyper-doc by a hyperlink or citation mark in its textual content . Given this essential distinction , hyperlinks or citations are worth specific modeling in many tasks such as link-based classification ( Lu and Getoor , 2003 ) , web retrieval ( Page et al . , 1999 ) , entity linking ( Cucerzan , 2007 ) , and citation recommendation ( He et al . , 2010 ) . To model hypertext documents , various efforts ( Cohn and Hofmann , 2000 ; Kataria et al . , 2010 ; Perozzi et al . , 2014 ; Zwicklbauer et al . , 2016 ; Wang et al . , 2016 ) have been made to depict networks of hyper-docs as well as their content . Among potential techniques , distributed representation ( Mikolov et al . , 2013 ; Le and Mikolov , 2014 ) tends to be promising since its validity and effectiveness are proven for plain documents on many natural language processing ( NLP ) tasks . Conventional attempts on utilizing embedding techniques in hyper-doc-related tasks generally fall into two types . The first type ( Berger et al . , 2017 ; Zwicklbauer et al . , 2016 ) simply downcasts hyper-docs to plain documents and feeds them into word2vec ( Mikolov et al . , 2013 ) ( w2v for short ) or doc2vec ( Le and Mikolov , 2014 ) ( d2v for short ) . These approaches involve downgrading hyperlinks and inevitably omit certain information in hyper-docs . However , no previous work investigates the information loss , and how it affects the performance of such downcasting-based adaptations . The second type designs sophisticated embedding models to fulfill certain tasks , e.g. , citation recommendation ( Huang et al . , 2015b ) , paper classification ( Wang et al . , 2016 ) , and entity linking ( Yamada et al . , 2016 ) , etc . These models are limited to specific tasks , and it is yet unknown whether embeddings learned for those particular tasks can generalize to others . Based on the above facts , we are interested in two questions : • What information should hyper-doc embedding models preserve , and what nice property should they possess ? • Is there a general approach to learning taskindependent embeddings of hyper-docs ? To answer the two questions , we formalize the hyper-doc embedding task , and propose four criteria , i.e. , content awareness , context awareness , newcomer friendliness , and context intent aware-ness , to assess different models . Then we discuss simple downcasting-based adaptations of existing approaches w.r.t . the above criteria , and demonstrate that none of them satisfy all four . To this end , we propose hyperdoc2vec ( h-d2v for short ) , a general embedding approach for hyperdocs . Different from most existing approaches , h-d2v learns two vectors for each hyper-doc to characterize its roles of citing others and being cited . Owning to this , h-d2v is able to directly model hyperlinks or citations without downgrading them . To evaluate the learned embeddings , we employ two tasks in the academic paper domain1 , i.e. , paper classification and citation recommendation . Experimental results demonstrate the superiority of h-d2v . Comparative studies and controlled experiments also confirm that h-d2v benefits from satisfying the above four criteria . We summarize our contributions as follows : • We propose four criteria to assess different hyper-document embedding models . • We propose hyperdoc2vec , a general embedding approach for hyper-documents . • We systematically conduct comparisons with competing approaches , validating the superiority of h-d2v in terms of the four criteria . We focus on the hyper-doc embedding problem . We propose that hyper-doc embedding algorithms should be content aware , context aware , newcomer friendly , and context intent aware . To meet all four criteria , we propose a general approach , hyperdoc2vec , which assigns two vectors to each hyper-doc and models citations in a straightforward manner . In doing so , the learned embeddings satisfy all criteria , which no existing model is able to . For evaluation , paper classification and citation recommendation are conducted on three academic paper datasets . Results confirm the effectiveness of our approach . Further analyses also demonstrate that possessing the four properties helps h-d2v outperform other models .
| sobamchan/aclsum | 1 | full_paper |
E06-1051 | We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appears , and ( ii ) the local contexts around the interacting entities . We performed experiments on extracting gene and protein interactions from two different data sets . The results show that our approach outperforms most of the previous methods based on syntactic and semantic information . Information Extraction ( IE ) is the process of finding relevant entities and their relationships within textual documents . Applications of IE range from Semantic Web to Bioinformatics . For example , there is an increasing interest in automatically extracting relevant information from biomedical literature . Recent evaluation campaigns on bio-entity recognition , such as BioCreAtIvE and JNLPBA 2004 shared task , have shown that several systems are able to achieve good performance ( even if it is a bit worse than that reported on news articles ) . However , relation identification is more useful from an applicative perspective but it is still a considerable challenge for automatic tools . In this work , we propose a supervised machine learning approach to relation extraction which is applicable even when ( deep ) linguistic processing is not available or reliable . In particular , we explore a kernel-based approach based solely on shallow linguistic processing , such as tokeniza-tion , sentence splitting , Part-of-Speech ( PoS ) tagging and lemmatization . Kernel methods ( Shawe-Taylor and Cristianini , 2004 ) show their full potential when an explicit computation of the feature map becomes computationally infeasible , due to the high or even infinite dimension of the feature space . For this reason , kernels have been recently used to develop innovative approaches to relation extraction based on syntactic information , in which the examples preserve their original representations ( i.e. parse trees ) and are compared by the kernel function ( Zelenko et al . , 2003 ; Culotta and Sorensen , 2004 ; Zhao and Grishman , 2005 ) . Despite the positive results obtained exploiting syntactic information , we claim that there is still room for improvement relying exclusively on shallow linguistic information for two main reasons . First of all , previous comparative evaluations put more stress on the deep linguistic approaches and did not put as much effort on developing effective methods based on shallow linguistic information . A second reason concerns the fact that syntactic parsing is not always robust enough to deal with real-world sentences . This may prevent approaches based on syntactic features from producing any result . Another related issue concerns the fact that parsers are available only for few languages and may not produce reliable results when used on domain specific texts ( as is the case of the biomedical literature ) . For example , most of the participants at the Learning Language in Logic ( LLL ) challenge on Genic Interaction Extraction ( see Section 4.2 ) were unable to successfully exploit linguistic information provided by parsers . It is still an open issue whether the use of domainspecific treebanks ( such as the Genia treebank 1 ) can be successfully exploited to overcome this problem . Therefore it is essential to better investigate the potential of approaches based exclusively on simple linguistic features . In our approach we use a combination of kernel functions to represent two distinct information sources : the global context where entities appear and their local contexts . The whole sentence where the entities appear ( global context ) is used to discover the presence of a relation between two entities , similarly to what was done by Bunescu and Mooney ( 2005b ) . Windows of limited size around the entities ( local contexts ) provide useful clues to identify the roles of the entities within a relation . The approach has some resemblance with what was proposed by Roth and Yih ( 2002 ) . The main difference is that we perform the extraction task in a single step via a combined kernel , while they used two separate classifiers to identify entities and relations and their output is later combined with a probabilistic global inference . We evaluated our relation extraction algorithm on two biomedical data sets ( i.e. the AImed corpus and the LLL challenge data set ; see Section 4 ) . The motivations for using these benchmarks derive from the increasing applicative interest in tools able to extract relations between relevant entities in biomedical texts and , consequently , from the growing availability of annotated data sets . The experiments show clearly that our approach consistently improves previous results . Surprisingly , it outperforms most of the systems based on syntactic or semantic information , even when this information is manually annotated ( i.e. the LLL challenge ) . The good results obtained using only shallow linguistic features provide a higher baseline against which it is possible to measure improvements obtained using methods based on deep linguistic processing . In the near future , we plan to extend our work in several ways . First , we would like to evaluate the contribution of syntactic information to relation extraction from biomedical literature . With this aim , we will integrate the output of a parser ( possibly trained on a domain-specific resource such the Genia Treebank ) . Second , we plan to test the portability of our model on ACE and MUC data sets . Third , we would like to use a named entity recognizer instead of assuming that entities are already extracted or given by a dictionary . Our long term goal is to populate databases and ontologies by extracting information from large text collections such as Medline . | They propose an approach for entity relation extraction using shallow linguistic information such as tokenization, sentence splitting, Part-of-Speech tagging and lemmatization coupled with kernel functions. | approach | coverage_first | What did the authors do?
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We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appears , and ( ii ) the local contexts around the interacting entities . We performed experiments on extracting gene and protein interactions from two different data sets . The results show that our approach outperforms most of the previous methods based on syntactic and semantic information . Information Extraction ( IE ) is the process of finding relevant entities and their relationships within textual documents . Applications of IE range from Semantic Web to Bioinformatics . For example , there is an increasing interest in automatically extracting relevant information from biomedical literature . Recent evaluation campaigns on bio-entity recognition , such as BioCreAtIvE and JNLPBA 2004 shared task , have shown that several systems are able to achieve good performance ( even if it is a bit worse than that reported on news articles ) . However , relation identification is more useful from an applicative perspective but it is still a considerable challenge for automatic tools . In this work , we propose a supervised machine learning approach to relation extraction which is applicable even when ( deep ) linguistic processing is not available or reliable . In particular , we explore a kernel-based approach based solely on shallow linguistic processing , such as tokeniza-tion , sentence splitting , Part-of-Speech ( PoS ) tagging and lemmatization . Kernel methods ( Shawe-Taylor and Cristianini , 2004 ) show their full potential when an explicit computation of the feature map becomes computationally infeasible , due to the high or even infinite dimension of the feature space . For this reason , kernels have been recently used to develop innovative approaches to relation extraction based on syntactic information , in which the examples preserve their original representations ( i.e. parse trees ) and are compared by the kernel function ( Zelenko et al . , 2003 ; Culotta and Sorensen , 2004 ; Zhao and Grishman , 2005 ) . Despite the positive results obtained exploiting syntactic information , we claim that there is still room for improvement relying exclusively on shallow linguistic information for two main reasons . First of all , previous comparative evaluations put more stress on the deep linguistic approaches and did not put as much effort on developing effective methods based on shallow linguistic information . A second reason concerns the fact that syntactic parsing is not always robust enough to deal with real-world sentences . This may prevent approaches based on syntactic features from producing any result . Another related issue concerns the fact that parsers are available only for few languages and may not produce reliable results when used on domain specific texts ( as is the case of the biomedical literature ) . For example , most of the participants at the Learning Language in Logic ( LLL ) challenge on Genic Interaction Extraction ( see Section 4.2 ) were unable to successfully exploit linguistic information provided by parsers . It is still an open issue whether the use of domainspecific treebanks ( such as the Genia treebank 1 ) can be successfully exploited to overcome this problem . Therefore it is essential to better investigate the potential of approaches based exclusively on simple linguistic features . In our approach we use a combination of kernel functions to represent two distinct information sources : the global context where entities appear and their local contexts . The whole sentence where the entities appear ( global context ) is used to discover the presence of a relation between two entities , similarly to what was done by Bunescu and Mooney ( 2005b ) . Windows of limited size around the entities ( local contexts ) provide useful clues to identify the roles of the entities within a relation . The approach has some resemblance with what was proposed by Roth and Yih ( 2002 ) . The main difference is that we perform the extraction task in a single step via a combined kernel , while they used two separate classifiers to identify entities and relations and their output is later combined with a probabilistic global inference . We evaluated our relation extraction algorithm on two biomedical data sets ( i.e. the AImed corpus and the LLL challenge data set ; see Section 4 ) . The motivations for using these benchmarks derive from the increasing applicative interest in tools able to extract relations between relevant entities in biomedical texts and , consequently , from the growing availability of annotated data sets . The experiments show clearly that our approach consistently improves previous results . Surprisingly , it outperforms most of the systems based on syntactic or semantic information , even when this information is manually annotated ( i.e. the LLL challenge ) . The good results obtained using only shallow linguistic features provide a higher baseline against which it is possible to measure improvements obtained using methods based on deep linguistic processing . In the near future , we plan to extend our work in several ways . First , we would like to evaluate the contribution of syntactic information to relation extraction from biomedical literature . With this aim , we will integrate the output of a parser ( possibly trained on a domain-specific resource such the Genia Treebank ) . Second , we plan to test the portability of our model on ACE and MUC data sets . Third , we would like to use a named entity recognizer instead of assuming that entities are already extracted or given by a dictionary . Our long term goal is to populate databases and ontologies by extracting information from large text collections such as Medline .
| sobamchan/aclsum | 2 | full_paper |
P18-1222 | Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general embedding approach for hyper-documents , namely , hyperdoc2vec , along with four criteria characterizing necessary information that hyper-document embedding models should preserve . Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks , i.e. , paper classification and citation recommendation , in the academic paper domain . Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t . the four criteria . The ubiquitous World Wide Web has boosted research interests on hypertext documents , e.g. , personal webpages ( Lu and Getoor , 2003 ) , Wikipedia pages ( Gabrilovich and Markovitch , 2007 ) , as well as academic papers ( Sugiyama and Kan , 2010 ) . Unlike independent plain documents , a hypertext document ( hyper-doc for short ) links to another hyper-doc by a hyperlink or citation mark in its textual content . Given this essential distinction , hyperlinks or citations are worth specific modeling in many tasks such as link-based classification ( Lu and Getoor , 2003 ) , web retrieval ( Page et al . , 1999 ) , entity linking ( Cucerzan , 2007 ) , and citation recommendation ( He et al . , 2010 ) . To model hypertext documents , various efforts ( Cohn and Hofmann , 2000 ; Kataria et al . , 2010 ; Perozzi et al . , 2014 ; Zwicklbauer et al . , 2016 ; Wang et al . , 2016 ) have been made to depict networks of hyper-docs as well as their content . Among potential techniques , distributed representation ( Mikolov et al . , 2013 ; Le and Mikolov , 2014 ) tends to be promising since its validity and effectiveness are proven for plain documents on many natural language processing ( NLP ) tasks . Conventional attempts on utilizing embedding techniques in hyper-doc-related tasks generally fall into two types . The first type ( Berger et al . , 2017 ; Zwicklbauer et al . , 2016 ) simply downcasts hyper-docs to plain documents and feeds them into word2vec ( Mikolov et al . , 2013 ) ( w2v for short ) or doc2vec ( Le and Mikolov , 2014 ) ( d2v for short ) . These approaches involve downgrading hyperlinks and inevitably omit certain information in hyper-docs . However , no previous work investigates the information loss , and how it affects the performance of such downcasting-based adaptations . The second type designs sophisticated embedding models to fulfill certain tasks , e.g. , citation recommendation ( Huang et al . , 2015b ) , paper classification ( Wang et al . , 2016 ) , and entity linking ( Yamada et al . , 2016 ) , etc . These models are limited to specific tasks , and it is yet unknown whether embeddings learned for those particular tasks can generalize to others . Based on the above facts , we are interested in two questions : • What information should hyper-doc embedding models preserve , and what nice property should they possess ? • Is there a general approach to learning taskindependent embeddings of hyper-docs ? To answer the two questions , we formalize the hyper-doc embedding task , and propose four criteria , i.e. , content awareness , context awareness , newcomer friendliness , and context intent aware-ness , to assess different models . Then we discuss simple downcasting-based adaptations of existing approaches w.r.t . the above criteria , and demonstrate that none of them satisfy all four . To this end , we propose hyperdoc2vec ( h-d2v for short ) , a general embedding approach for hyperdocs . Different from most existing approaches , h-d2v learns two vectors for each hyper-doc to characterize its roles of citing others and being cited . Owning to this , h-d2v is able to directly model hyperlinks or citations without downgrading them . To evaluate the learned embeddings , we employ two tasks in the academic paper domain1 , i.e. , paper classification and citation recommendation . Experimental results demonstrate the superiority of h-d2v . Comparative studies and controlled experiments also confirm that h-d2v benefits from satisfying the above four criteria . We summarize our contributions as follows : • We propose four criteria to assess different hyper-document embedding models . • We propose hyperdoc2vec , a general embedding approach for hyper-documents . • We systematically conduct comparisons with competing approaches , validating the superiority of h-d2v in terms of the four criteria . We focus on the hyper-doc embedding problem . We propose that hyper-doc embedding algorithms should be content aware , context aware , newcomer friendly , and context intent aware . To meet all four criteria , we propose a general approach , hyperdoc2vec , which assigns two vectors to each hyper-doc and models citations in a straightforward manner . In doing so , the learned embeddings satisfy all criteria , which no existing model is able to . For evaluation , paper classification and citation recommendation are conducted on three academic paper datasets . Results confirm the effectiveness of our approach . Further analyses also demonstrate that possessing the four properties helps h-d2v outperform other models . | Existing text embedding methods do not take structures of hyper-documents into account losing useful properties for downstream tasks. | challenge | coverage_first | State the difficulty of the study.
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Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general embedding approach for hyper-documents , namely , hyperdoc2vec , along with four criteria characterizing necessary information that hyper-document embedding models should preserve . Systematic comparisons are conducted between hyperdoc2vec and several competitors on two tasks , i.e. , paper classification and citation recommendation , in the academic paper domain . Analyses and experiments both validate the superiority of hyperdoc2vec to other models w.r.t . the four criteria . The ubiquitous World Wide Web has boosted research interests on hypertext documents , e.g. , personal webpages ( Lu and Getoor , 2003 ) , Wikipedia pages ( Gabrilovich and Markovitch , 2007 ) , as well as academic papers ( Sugiyama and Kan , 2010 ) . Unlike independent plain documents , a hypertext document ( hyper-doc for short ) links to another hyper-doc by a hyperlink or citation mark in its textual content . Given this essential distinction , hyperlinks or citations are worth specific modeling in many tasks such as link-based classification ( Lu and Getoor , 2003 ) , web retrieval ( Page et al . , 1999 ) , entity linking ( Cucerzan , 2007 ) , and citation recommendation ( He et al . , 2010 ) . To model hypertext documents , various efforts ( Cohn and Hofmann , 2000 ; Kataria et al . , 2010 ; Perozzi et al . , 2014 ; Zwicklbauer et al . , 2016 ; Wang et al . , 2016 ) have been made to depict networks of hyper-docs as well as their content . Among potential techniques , distributed representation ( Mikolov et al . , 2013 ; Le and Mikolov , 2014 ) tends to be promising since its validity and effectiveness are proven for plain documents on many natural language processing ( NLP ) tasks . Conventional attempts on utilizing embedding techniques in hyper-doc-related tasks generally fall into two types . The first type ( Berger et al . , 2017 ; Zwicklbauer et al . , 2016 ) simply downcasts hyper-docs to plain documents and feeds them into word2vec ( Mikolov et al . , 2013 ) ( w2v for short ) or doc2vec ( Le and Mikolov , 2014 ) ( d2v for short ) . These approaches involve downgrading hyperlinks and inevitably omit certain information in hyper-docs . However , no previous work investigates the information loss , and how it affects the performance of such downcasting-based adaptations . The second type designs sophisticated embedding models to fulfill certain tasks , e.g. , citation recommendation ( Huang et al . , 2015b ) , paper classification ( Wang et al . , 2016 ) , and entity linking ( Yamada et al . , 2016 ) , etc . These models are limited to specific tasks , and it is yet unknown whether embeddings learned for those particular tasks can generalize to others . Based on the above facts , we are interested in two questions : • What information should hyper-doc embedding models preserve , and what nice property should they possess ? • Is there a general approach to learning taskindependent embeddings of hyper-docs ? To answer the two questions , we formalize the hyper-doc embedding task , and propose four criteria , i.e. , content awareness , context awareness , newcomer friendliness , and context intent aware-ness , to assess different models . Then we discuss simple downcasting-based adaptations of existing approaches w.r.t . the above criteria , and demonstrate that none of them satisfy all four . To this end , we propose hyperdoc2vec ( h-d2v for short ) , a general embedding approach for hyperdocs . Different from most existing approaches , h-d2v learns two vectors for each hyper-doc to characterize its roles of citing others and being cited . Owning to this , h-d2v is able to directly model hyperlinks or citations without downgrading them . To evaluate the learned embeddings , we employ two tasks in the academic paper domain1 , i.e. , paper classification and citation recommendation . Experimental results demonstrate the superiority of h-d2v . Comparative studies and controlled experiments also confirm that h-d2v benefits from satisfying the above four criteria . We summarize our contributions as follows : • We propose four criteria to assess different hyper-document embedding models . • We propose hyperdoc2vec , a general embedding approach for hyper-documents . • We systematically conduct comparisons with competing approaches , validating the superiority of h-d2v in terms of the four criteria . We focus on the hyper-doc embedding problem . We propose that hyper-doc embedding algorithms should be content aware , context aware , newcomer friendly , and context intent aware . To meet all four criteria , we propose a general approach , hyperdoc2vec , which assigns two vectors to each hyper-doc and models citations in a straightforward manner . In doing so , the learned embeddings satisfy all criteria , which no existing model is able to . For evaluation , paper classification and citation recommendation are conducted on three academic paper datasets . Results confirm the effectiveness of our approach . Further analyses also demonstrate that possessing the four properties helps h-d2v outperform other models .
| sobamchan/aclsum | 2 | full_paper |
P16-1067 | This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships . An unsupervised learning method is developed to initialize the HMM parameters . Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests . As an example of its applications , the proposed approach is applied for attributing authorship of a document and has also shown promising results . Authorship analysis is a process of inspecting documents in order to extract authorial information about these documents . It is considered as a general concept that embraces several types of authorship subjects , including authorship verif ication , plagiarism detection and author attribution . Authorship verification ( Brocardo et al . , 2013 ; Potha and Stamatatos , 2014 ) decides whether a given document is written by a specific author . Plagiarism detection ( Stein et al . , 2011 ; Kestemont et al . , 2011 ) seeks to expose the similarity between two texts . However , it is un-able to determine if they are written by the same author . In author attribution ( Juola , 2006 ; Savoy , 2015 ) , a real author of an anonymous document is predicted using labeled documents of a set of candidate authors . Another significant subject in authorship analysis , which has received comparatively less attention from research community , is authorship-based document decomposition ( ABDD ) . This subject is to group the sentences of a multi-author document to different classes , of which each contains the sentences written by only one author . Many applications can take advantage of such a subject , especially those in forensic investigation , which aim to determine the authorship of sentences in a multi-author document . Furthermore , this kind of subject is beneficial for detecting plagiarism in a document and defining contributions of authors in a multi-author document for commercial purpose . ABDD can also be applied to identify which source ( regarded as an ' author ' in this paper ) a part of a document is copied from when the document is formed by taking contents from various sources . In despite of the benefits of ABDD , there has been little research reported on this subject . Koppel et al . ( 2011 ) are the first researchers who implemented an unsupervised approach for ABDD . However , their approach is restricted to Hebrew documents only . The authors of Akiva and Koppel ( 2013 ) addressed the drawbacks of the above approach by proposing a generic unsupervised approach for ABDD . Their approach utilized distance measurements to increase the precision and accuracy of clustering and classification phases , respectively . The accuracy of their approach is highly dependent on the number of au-thors . When the number of authors increases , the accuracy of the approach is significantly dropped . Giannella ( 2015 ) presented an improved approach for ABDD when the number of authors of the document is known or unknown . In his approach , a Bayesian segmentation algorithm is applied , which is followed by a segment clustering algorithm . However , the author tested his approach by using only documents with a few transitions among authors . Furthermore , the accuracy of the approach is very sensitive to the setting of its parameters . In Aldebei et al . ( 2015 ) , the authors presented an unsupervised approach ABDD by exploiting the differences in the posterior probabilities of a Naive-Bayesian model in order to increase the precision and the classification accuracy , and to be less dependent on the number of authors in comparing with the approach in Akiva and Koppel ( 2013 ) . Their work was tested on documents with up to 400 transitions among authors and the accuracy of their approach was not sensitive to the setting of parameters , in contrast with the approach in Giannella ( 2015 ) . However , the performance of their approach greatly depends on a threshold , of which the optimal value for an individual document is not easy to find . Some other works have focused on segmenting a document into components according to their topics . For applications where the topics of documents are unavailable , these topicbased solutions will fail . In this paper , the ABDD approach is independent of documents ' topics . All of the existing works have assumed that the observations ( i.e. , sentences ) are independent and identically distributed ( i.i.d . ) . No consideration has been given to the contextual information between the observations . However , in some cases , the i.i.d . assumption is deemed as a poor one ( Rogovschi et al . , 2010 ) . In this paper , we will relax this assumption and consider sentences of a document as a sequence of observations . We make use of the contextual information hidden between sentences in order to identify the authorship of each sentence in a document . In other words , the authorships of the " previous " and " subsequent " sentences have relationships with the authorship of the current sentence . There-fore , in this paper , a well-known sequential model , Hidden Markov Model ( HMM ) , is used for modelling the sequential patterns of the document in order to describe the authorship relationships . The contributions of this article are summarized as follows . 1 . We capture the dependencies between consecutive elements in a document to identify different authorial components and construct an HMM for classification . It is for the first time the sequential patterns hidden among document elements is considered for such a problem . 2 . To build and learn the HMM model , an unsupervised learning method is first proposed to estimate its initial parameters , and it does not require any information of authors or document 's context other than how many authors have contributed to write the document . 3 . Different from the approach in Aldebei et al . ( 2015 ) , the proposed unsupervised approach no longer relies on any predetermined threshold for ABDD . 4 . Comprehensive experiments are conducted to demonstrate the superior performance of our ideas on both widely-used artificial benchmark datasets and an authentic scientific document . As an example of its applications , the proposed approach is also applied for attributing authorship on a popular dataset . The proposed approach can not only correctly determine the author of a disputed document but also provide a way for measuring the confidence level of the authorship decision for the first time . The rest of this article is organised as follows . Section 2 reviews the HMM . Section 3 presents the details of our proposed approach , including the processes for initialization and learning of HMM parameters , and the Viterbi decoding process for classification . Experiments are conducted in Section 4 , followed by the conclusion in Section 5 . We have developed an unsupervised approach for decomposing a multi-author document based on authorship . Different from the stateof-the-art approaches , we have innovatively made use of the sequential information hidden among document elements . For this purpose , we have used HMM and constructed a sequential probabilistic model , which is used to find the best sequence of authors that represents the sentences of the document . An unsupervised learning method has also been developed to estimate the initial parameter values of HMM . Comparative experiments conducted on benchmark datasets have demonstrated the effectiveness of our ideas with superior perfor-mance achieved on both artificial and authentic documents . An application of the proposed approach on authorship attribution has also achieved perfect results of 100 % accuracies together with confidence measurement for the first time . | They propose a HMM-based sequential probabilistic model that captures the dependencies of sequential sentences and their authors coupled with an unsupervised initialization method. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
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This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships . An unsupervised learning method is developed to initialize the HMM parameters . Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests . As an example of its applications , the proposed approach is applied for attributing authorship of a document and has also shown promising results . Authorship analysis is a process of inspecting documents in order to extract authorial information about these documents . It is considered as a general concept that embraces several types of authorship subjects , including authorship verif ication , plagiarism detection and author attribution . Authorship verification ( Brocardo et al . , 2013 ; Potha and Stamatatos , 2014 ) decides whether a given document is written by a specific author . Plagiarism detection ( Stein et al . , 2011 ; Kestemont et al . , 2011 ) seeks to expose the similarity between two texts . However , it is un-able to determine if they are written by the same author . In author attribution ( Juola , 2006 ; Savoy , 2015 ) , a real author of an anonymous document is predicted using labeled documents of a set of candidate authors . Another significant subject in authorship analysis , which has received comparatively less attention from research community , is authorship-based document decomposition ( ABDD ) . This subject is to group the sentences of a multi-author document to different classes , of which each contains the sentences written by only one author . Many applications can take advantage of such a subject , especially those in forensic investigation , which aim to determine the authorship of sentences in a multi-author document . Furthermore , this kind of subject is beneficial for detecting plagiarism in a document and defining contributions of authors in a multi-author document for commercial purpose . ABDD can also be applied to identify which source ( regarded as an ' author ' in this paper ) a part of a document is copied from when the document is formed by taking contents from various sources . In despite of the benefits of ABDD , there has been little research reported on this subject . Koppel et al . ( 2011 ) are the first researchers who implemented an unsupervised approach for ABDD . However , their approach is restricted to Hebrew documents only . The authors of Akiva and Koppel ( 2013 ) addressed the drawbacks of the above approach by proposing a generic unsupervised approach for ABDD . Their approach utilized distance measurements to increase the precision and accuracy of clustering and classification phases , respectively . The accuracy of their approach is highly dependent on the number of au-thors . When the number of authors increases , the accuracy of the approach is significantly dropped . Giannella ( 2015 ) presented an improved approach for ABDD when the number of authors of the document is known or unknown . In his approach , a Bayesian segmentation algorithm is applied , which is followed by a segment clustering algorithm . However , the author tested his approach by using only documents with a few transitions among authors . Furthermore , the accuracy of the approach is very sensitive to the setting of its parameters . In Aldebei et al . ( 2015 ) , the authors presented an unsupervised approach ABDD by exploiting the differences in the posterior probabilities of a Naive-Bayesian model in order to increase the precision and the classification accuracy , and to be less dependent on the number of authors in comparing with the approach in Akiva and Koppel ( 2013 ) . Their work was tested on documents with up to 400 transitions among authors and the accuracy of their approach was not sensitive to the setting of parameters , in contrast with the approach in Giannella ( 2015 ) . However , the performance of their approach greatly depends on a threshold , of which the optimal value for an individual document is not easy to find . Some other works have focused on segmenting a document into components according to their topics . For applications where the topics of documents are unavailable , these topicbased solutions will fail . In this paper , the ABDD approach is independent of documents ' topics . All of the existing works have assumed that the observations ( i.e. , sentences ) are independent and identically distributed ( i.i.d . ) . No consideration has been given to the contextual information between the observations . However , in some cases , the i.i.d . assumption is deemed as a poor one ( Rogovschi et al . , 2010 ) . In this paper , we will relax this assumption and consider sentences of a document as a sequence of observations . We make use of the contextual information hidden between sentences in order to identify the authorship of each sentence in a document . In other words , the authorships of the " previous " and " subsequent " sentences have relationships with the authorship of the current sentence . There-fore , in this paper , a well-known sequential model , Hidden Markov Model ( HMM ) , is used for modelling the sequential patterns of the document in order to describe the authorship relationships . The contributions of this article are summarized as follows . 1 . We capture the dependencies between consecutive elements in a document to identify different authorial components and construct an HMM for classification . It is for the first time the sequential patterns hidden among document elements is considered for such a problem . 2 . To build and learn the HMM model , an unsupervised learning method is first proposed to estimate its initial parameters , and it does not require any information of authors or document 's context other than how many authors have contributed to write the document . 3 . Different from the approach in Aldebei et al . ( 2015 ) , the proposed unsupervised approach no longer relies on any predetermined threshold for ABDD . 4 . Comprehensive experiments are conducted to demonstrate the superior performance of our ideas on both widely-used artificial benchmark datasets and an authentic scientific document . As an example of its applications , the proposed approach is also applied for attributing authorship on a popular dataset . The proposed approach can not only correctly determine the author of a disputed document but also provide a way for measuring the confidence level of the authorship decision for the first time . The rest of this article is organised as follows . Section 2 reviews the HMM . Section 3 presents the details of our proposed approach , including the processes for initialization and learning of HMM parameters , and the Viterbi decoding process for classification . Experiments are conducted in Section 4 , followed by the conclusion in Section 5 . We have developed an unsupervised approach for decomposing a multi-author document based on authorship . Different from the stateof-the-art approaches , we have innovatively made use of the sequential information hidden among document elements . For this purpose , we have used HMM and constructed a sequential probabilistic model , which is used to find the best sequence of authors that represents the sentences of the document . An unsupervised learning method has also been developed to estimate the initial parameter values of HMM . Comparative experiments conducted on benchmark datasets have demonstrated the effectiveness of our ideas with superior perfor-mance achieved on both artificial and authentic documents . An application of the proposed approach on authorship attribution has also achieved perfect results of 100 % accuracies together with confidence measurement for the first time .
| sobamchan/aclsum | 2 | full_paper |
N18-1114 | We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -the Tensor Product Generation Network ( TPGN ) -is proposed which is capable in principle of carrying out TPR computation , but which uses unconstrained deep learning to design its internal representations . Instantiated in a model for image-caption generation , TPGN outperforms LSTM baselines when evaluated on the COCO dataset . The TPR-capable structure enables interpretation of internal representations and operations , which prove to contain considerable grammatical content . Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation . In this paper we introduce a new architecture for natural language processing ( NLP ) . On what type of principles can a computational architecture be founded ? It would seem a sound principle to require that the hypothesis space for learning which an architecture provides include network hypotheses that are independently known to be suitable for performing the target task . Our proposed architecture makes available to deep learning network configurations that perform natural language generation by use of Tensor Product Representations ( TPRs ) ( Smolensky and Legendre , 2006 ) . Whether learning will create TPRs is unknown in advance , but what we can say with certainty is that the hypothesis space being searched during learn-ing includes TPRs as one appropriate solution to the problem . TPRs are a general method for generating vector-space embeddings of complex symbol structures . Prior work has proved that TPRs enable powerful symbol processing to be carried out using neural network computation ( Smolensky , 2012 ) . This includes generating parse trees that conform to a grammar ( Cho et al . , 2017 ) , although incorporating such capabilities into deep learning networks such as those developed here remains for future work . The architecture presented here relies on simpler use of TPRs to generate sentences ; grammars are not explicitly encoded here . We test the proposed architecture by applying it to image-caption generation ( on the MS-COCO dataset , ( COCO , 2017 ) ) . The results improve upon a baseline deploying a state-of-the-art LSTM architecture ( Vinyals et al . , 2015 ) , and the TPR foundations of the architecture provide greater interpretability . Section 2 of the paper reviews TPR . Section 3 presents the proposed architecture , the Tensor Product Generation Network ( TPGN ) . Section 4 describes the particular model we study for image captioning , and Section 5 presents the experimental results . Importantly , what the model has learned is interpreted in Section 5.3 . Section 6 discusses the relation of the new model to previous work and Section 7 concludes . Tensor Product Representation ( TPR ) ( Smolensky , 1990 ) is a general technique for constructing vector embeddings of complex symbol structures in such a way that powerful symbolic functions can be computed using hand-designed neural network computation . Integrating TPR with deep learning is a largely open problem for which the work presented here proposes a general approach : design deep architectures that are TPRcapable -TPR computation is within the scope of the capabilities of the architecture in principle . For natural language generation , we proposed such an architecture , the Tensor Product Generation Network ( TPGN ): it embodies the TPR operation of unbinding which is used to extract particular symbols ( e.g. , words ) from complex structures ( e.g. , sentences ) . The architecture can be interpreted as containing a part that encodes a sentence and a part that selects one structural role at a time to extract from the sentence . We applied the approach to image-caption generation , developing a TPGN model that was evaluated on the COCO dataset , on which it outperformed LSTM baselines on a range of standard metrics . Unlike standard LSTMs , however , the TPGN model admits a level of interpretability : we can see which roles are being unbound by the unbinding vectors generated internally within the model . We find such roles contain considerable grammatical information , enabling POS tag prediction for the words they generate and displaying clustering by POS . | They propose a newly designed model that is based on Tensor Product Representations for encoding and processing words and sentences. | approach | coverage_first | Give the approach described.
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We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -the Tensor Product Generation Network ( TPGN ) -is proposed which is capable in principle of carrying out TPR computation , but which uses unconstrained deep learning to design its internal representations . Instantiated in a model for image-caption generation , TPGN outperforms LSTM baselines when evaluated on the COCO dataset . The TPR-capable structure enables interpretation of internal representations and operations , which prove to contain considerable grammatical content . Our caption-generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation . In this paper we introduce a new architecture for natural language processing ( NLP ) . On what type of principles can a computational architecture be founded ? It would seem a sound principle to require that the hypothesis space for learning which an architecture provides include network hypotheses that are independently known to be suitable for performing the target task . Our proposed architecture makes available to deep learning network configurations that perform natural language generation by use of Tensor Product Representations ( TPRs ) ( Smolensky and Legendre , 2006 ) . Whether learning will create TPRs is unknown in advance , but what we can say with certainty is that the hypothesis space being searched during learn-ing includes TPRs as one appropriate solution to the problem . TPRs are a general method for generating vector-space embeddings of complex symbol structures . Prior work has proved that TPRs enable powerful symbol processing to be carried out using neural network computation ( Smolensky , 2012 ) . This includes generating parse trees that conform to a grammar ( Cho et al . , 2017 ) , although incorporating such capabilities into deep learning networks such as those developed here remains for future work . The architecture presented here relies on simpler use of TPRs to generate sentences ; grammars are not explicitly encoded here . We test the proposed architecture by applying it to image-caption generation ( on the MS-COCO dataset , ( COCO , 2017 ) ) . The results improve upon a baseline deploying a state-of-the-art LSTM architecture ( Vinyals et al . , 2015 ) , and the TPR foundations of the architecture provide greater interpretability . Section 2 of the paper reviews TPR . Section 3 presents the proposed architecture , the Tensor Product Generation Network ( TPGN ) . Section 4 describes the particular model we study for image captioning , and Section 5 presents the experimental results . Importantly , what the model has learned is interpreted in Section 5.3 . Section 6 discusses the relation of the new model to previous work and Section 7 concludes . Tensor Product Representation ( TPR ) ( Smolensky , 1990 ) is a general technique for constructing vector embeddings of complex symbol structures in such a way that powerful symbolic functions can be computed using hand-designed neural network computation . Integrating TPR with deep learning is a largely open problem for which the work presented here proposes a general approach : design deep architectures that are TPRcapable -TPR computation is within the scope of the capabilities of the architecture in principle . For natural language generation , we proposed such an architecture , the Tensor Product Generation Network ( TPGN ): it embodies the TPR operation of unbinding which is used to extract particular symbols ( e.g. , words ) from complex structures ( e.g. , sentences ) . The architecture can be interpreted as containing a part that encodes a sentence and a part that selects one structural role at a time to extract from the sentence . We applied the approach to image-caption generation , developing a TPGN model that was evaluated on the COCO dataset , on which it outperformed LSTM baselines on a range of standard metrics . Unlike standard LSTMs , however , the TPGN model admits a level of interpretability : we can see which roles are being unbound by the unbinding vectors generated internally within the model . We find such roles contain considerable grammatical information , enabling POS tag prediction for the words they generate and displaying clustering by POS .
| sobamchan/aclsum | 1 | full_paper |
2020.acl-main.528 | Recently , many works have tried to augment the performance of Chinese named entity recognition ( NER ) using word lexicons . As a representative , Lattice-LSTM ( Zhang and Yang , 2018 ) has achieved new benchmark results on several public Chinese NER datasets . However , Lattice-LSTM has a complex model architecture . This limits its application in many industrial areas where real-time NER responses are needed . In this work , we propose a simple but effective method for incorporating the word lexicon into the character representations . This method avoids designing a complicated sequence modeling architecture , and for any neural NER model , it requires only subtle adjustment of the character representation layer to introduce the lexicon information . Experimental studies on four benchmark Chinese NER datasets show that our method achieves an inference speed up to 6.15 times faster than those of state-ofthe-art methods , along with a better performance . The experimental results also show that the proposed method can be easily incorporated with pre-trained models like BERT . 1 Named Entity Recognition ( NER ) is concerned with the identification of named entities , such as persons , locations , and organizations , in unstructured text . NER plays an important role in many downstream tasks , including knowledge base construction ( Riedel et al . , 2013 ) , information retrieval ( Chen et al . , 2015 ) , and question answering ( Diefenbach et al . , 2018 ) . In languages where words are naturally separated ( e.g. , English ) , NER has been conventionally formulated as a sequence labeling problem , and the state-of-the-art results have been achieved using neural-network-based models ( Huang et al . , 2015 ; Chiu and Nichols , 2016 ; Liu et al . , 2018 ) . Compared with NER in English , Chinese NER is more difficult since sentences in Chinese are not naturally segmented . Thus , a common practice for Chinese NER is to first perform word segmentation using an existing CWS system and then apply a word-level sequence labeling model to the segmented sentence ( Yang et al . , 2016 ; He and Sun , 2017b ) . However , it is inevitable that the CWS system will incorrectly segment query sentences . This will result in errors in the detection of entity boundary and the prediction of entity category in NER . Therefore , some approaches resort to performing Chinese NER directly at the character level , which has been empirically proven to be effective ( He and Wang , 2008 ; Liu et al . , 2010 ; Li et al . , 2014 ; Liu et al . , 2019 ; Sui et al . , 2019 ; Gui et al . , 2019b ; Ding et al . , 2019 ) . A drawback of the purely character-based NER method is that the word information is not fully exploited . With this consideration , Zhang and Yang , ( 2018 ) proposed Lattice-LSTM for incorporating word lexicons into the character-based NER model . Moreover , rather than heuristically choosing a word for the character when it matches multiple words in the lexicon , the authors proposed to preserve all words that match the character , leaving the subsequent NER model to determine which word to apply . To realize this idea , they introduced an elaborate modification to the sequence modeling layer of the LSTM-CRF model ( Huang et al . , 2015 ) . Experimental studies on four Chinese NER datasets have verified the effectiveness of Lattice-LSTM . However , the model architecture of Lattice-LSTM is quite complicated . In order to introduce lexicon information , Lattice-LSTM adds several additional edges between nonadjacent characters in the input sequence , which significantly slows its training and inference speeds . In addition , it is difficult to transfer the structure of Lattice-LSTM to other neural-network architectures ( e.g. , convolutional neural networks and transformers ) that may be more suitable for some specific tasks . In this work , we propose a simpler method to realize the idea of Lattice-LSTM , i.e. , incorporating all the matched words for each character to a character-based NER model . The first principle of our model design is to achieve a fast inference speed . To this end , we propose to encode lexicon information in the character representations , and we design the encoding scheme to preserve as much of the lexicon matching results as possible . Compared with Lattice-LSTM , our method avoids the need for a complicated model architecture , is easier to implement , and can be quickly adapted to any appropriate neural NER model by adjusting the character representation layer . In addition , ablation studies show the superiority of our method in incorporating more complete and distinct lexicon information , as well as introducing a more effective word-weighting strategy . The contributions of this work can be summarized as follows : • We propose a simple but effective method for incorporating word lexicons into the character representations for Chinese NER . • The proposed method is transferable to different sequence-labeling architectures and can be easily incorporated with pre-trained models like BERT ( Devlin et al . , 2018 ) . We performed experiments on four public Chinese NER datasets . The experimental results show that when implementing the sequence modeling layer with a single-layer Bi-LSTM , our method achieves considerable improvements over the state-of-theart methods in both inference speed and sequence labeling performance . In this work , we addressed the computational efficiency of utilizing word lexicons in Chinese NER . To obtain a high-performing Chinese NER system with a fast inference speed , we proposed a novel method to incorporate the lexicon information into the character representations . Experimental studies on four benchmark Chinese NER datasets reveal that our method can achieve a much faster inference speed and better performance than the compared state-of-the-art methods . | Named entity recognition in Chinese requires word segmentation causes errors or character-level model with lexical features that is complex and expensive. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
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Recently , many works have tried to augment the performance of Chinese named entity recognition ( NER ) using word lexicons . As a representative , Lattice-LSTM ( Zhang and Yang , 2018 ) has achieved new benchmark results on several public Chinese NER datasets . However , Lattice-LSTM has a complex model architecture . This limits its application in many industrial areas where real-time NER responses are needed . In this work , we propose a simple but effective method for incorporating the word lexicon into the character representations . This method avoids designing a complicated sequence modeling architecture , and for any neural NER model , it requires only subtle adjustment of the character representation layer to introduce the lexicon information . Experimental studies on four benchmark Chinese NER datasets show that our method achieves an inference speed up to 6.15 times faster than those of state-ofthe-art methods , along with a better performance . The experimental results also show that the proposed method can be easily incorporated with pre-trained models like BERT . 1 Named Entity Recognition ( NER ) is concerned with the identification of named entities , such as persons , locations , and organizations , in unstructured text . NER plays an important role in many downstream tasks , including knowledge base construction ( Riedel et al . , 2013 ) , information retrieval ( Chen et al . , 2015 ) , and question answering ( Diefenbach et al . , 2018 ) . In languages where words are naturally separated ( e.g. , English ) , NER has been conventionally formulated as a sequence labeling problem , and the state-of-the-art results have been achieved using neural-network-based models ( Huang et al . , 2015 ; Chiu and Nichols , 2016 ; Liu et al . , 2018 ) . Compared with NER in English , Chinese NER is more difficult since sentences in Chinese are not naturally segmented . Thus , a common practice for Chinese NER is to first perform word segmentation using an existing CWS system and then apply a word-level sequence labeling model to the segmented sentence ( Yang et al . , 2016 ; He and Sun , 2017b ) . However , it is inevitable that the CWS system will incorrectly segment query sentences . This will result in errors in the detection of entity boundary and the prediction of entity category in NER . Therefore , some approaches resort to performing Chinese NER directly at the character level , which has been empirically proven to be effective ( He and Wang , 2008 ; Liu et al . , 2010 ; Li et al . , 2014 ; Liu et al . , 2019 ; Sui et al . , 2019 ; Gui et al . , 2019b ; Ding et al . , 2019 ) . A drawback of the purely character-based NER method is that the word information is not fully exploited . With this consideration , Zhang and Yang , ( 2018 ) proposed Lattice-LSTM for incorporating word lexicons into the character-based NER model . Moreover , rather than heuristically choosing a word for the character when it matches multiple words in the lexicon , the authors proposed to preserve all words that match the character , leaving the subsequent NER model to determine which word to apply . To realize this idea , they introduced an elaborate modification to the sequence modeling layer of the LSTM-CRF model ( Huang et al . , 2015 ) . Experimental studies on four Chinese NER datasets have verified the effectiveness of Lattice-LSTM . However , the model architecture of Lattice-LSTM is quite complicated . In order to introduce lexicon information , Lattice-LSTM adds several additional edges between nonadjacent characters in the input sequence , which significantly slows its training and inference speeds . In addition , it is difficult to transfer the structure of Lattice-LSTM to other neural-network architectures ( e.g. , convolutional neural networks and transformers ) that may be more suitable for some specific tasks . In this work , we propose a simpler method to realize the idea of Lattice-LSTM , i.e. , incorporating all the matched words for each character to a character-based NER model . The first principle of our model design is to achieve a fast inference speed . To this end , we propose to encode lexicon information in the character representations , and we design the encoding scheme to preserve as much of the lexicon matching results as possible . Compared with Lattice-LSTM , our method avoids the need for a complicated model architecture , is easier to implement , and can be quickly adapted to any appropriate neural NER model by adjusting the character representation layer . In addition , ablation studies show the superiority of our method in incorporating more complete and distinct lexicon information , as well as introducing a more effective word-weighting strategy . The contributions of this work can be summarized as follows : • We propose a simple but effective method for incorporating word lexicons into the character representations for Chinese NER . • The proposed method is transferable to different sequence-labeling architectures and can be easily incorporated with pre-trained models like BERT ( Devlin et al . , 2018 ) . We performed experiments on four public Chinese NER datasets . The experimental results show that when implementing the sequence modeling layer with a single-layer Bi-LSTM , our method achieves considerable improvements over the state-of-theart methods in both inference speed and sequence labeling performance . In this work , we addressed the computational efficiency of utilizing word lexicons in Chinese NER . To obtain a high-performing Chinese NER system with a fast inference speed , we proposed a novel method to incorporate the lexicon information into the character representations . Experimental studies on four benchmark Chinese NER datasets reveal that our method can achieve a much faster inference speed and better performance than the compared state-of-the-art methods .
| sobamchan/aclsum | 0 | full_paper |
E09-1032 | We explore the problem of resolving the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . First , we distinguish generic and referential uses , then we classify the referential uses as either plural or singular , and finally , for the latter cases , we identify the addressee . In our first set of experiments , the linguistic and visual features are derived from manual transcriptions and annotations , but in the second set , they are generated through entirely automatic means . Results show that a multimodal system is often preferable to a unimodal one . The English pronoun you is the second most frequent word in unrestricted conversation ( after I and right before it ) . 1 Despite this , with the exception of Gupta et al . ( 2007b ; 2007a ) , its resolution has received very little attention in the literature . This is perhaps not surprising since the vast amount of work on anaphora and reference resolution has focused on text or discourse -mediums where second-person deixis is perhaps not as prominent as it is in dialogue . For spoken dialogue pronoun resolution modules however , resolving you is an essential task that has an important impact on the capabilities of dialogue summarization systems . Besides being important for computational implementations , resolving you is also an interesting and challenging research problem . As for third person pronouns such as it , some uses of you are not strictly referential . These include discourse marker uses such as you know in example ( 1 ) , and generic uses like ( 2 ) , where you does not refer to the addressee as it does in ( 3 ) . ( 1 ) It 's not just , you know , noises like something hitting . ( 2 ) Often , you need to know specific button sequences to get certain functionalities done . ( 3 ) I think it 's good . You 've done a good review . However , unlike it , you is ambiguous between singular and plural interpretations -an issue that is particularly problematic in multi-party conversations . While you clearly has a plural referent in ( 4 ) , in ( 3 ) the number of its referent is ambiguous . 2(4 ) I do n't know if you guys have any questions . When an utterance contains a singular referential you , resolving the you amounts to identifying the individual to whom the utterance is addressed . This is trivial in two-person dialogue since the current listener is always the addressee , but in conversations with multiple participants , it is a complex problem where different kinds of linguistic and visual information play important roles ( Jovanovic , 2007 ) . One of the issues we investigate here is how this applies to the more concrete problem of resolving the second person pronoun you . We approach this issue as a three-step problem . Using the AMI Meeting Corpus ( McCowan et al . , 2005 ) of multi-party dialogues , we first discriminate between referential and generic uses of you . Then , within the referential uses , we distinguish between singular and plural , and finally , we resolve the singular referential instances by identifying the intended addressee . We use multimodal features : initially , we extract discourse features from manual transcriptions and use visual information derived from manual annotations , but then we move to a fully automatic approach , using 1-best transcriptions produced by an automatic speech recognizer ( ASR ) and visual features automatically extracted from raw video . In the next section of this paper , we give a brief overview of related work . We describe our data in Section 3 , and explain how we extract visual and linguistic features in Sections 4 and 5 respectively . Section 6 then presents our experiments with manual transcriptions and annotations , while Section 7 , those with automatically extracted information . We end with conclusions in Section 8 . We have investigated the automatic resolution of the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . We conducted a first set of experiments where our features were derived from manual transcriptions and annotations , and then a second set where they were generated by entirely automatic means . To our knowledge , this is the first attempt at tackling this problem using automatically extracted multimodal information . Our experiments showed that visual information can be highly predictive in resolving the addressee of singular referential uses of you . Visual features significantly improved the performance of both our manual and automatic systems , and the latter achieved an encouraging 75 % accuracy . We also found that our visual features had predictive power for distinguishing between generic and referential uses of you , and between referential singulars and plurals . Indeed , for the latter task , they significantly improved the manual system 's performance . The listeners ' gaze features were useful here : in our data set it was apparently the case that the speaker would often use the whiteboard / projector screen when addressing the group , thus drawing the listeners ' gaze in this direction . Future work will involve expanding our dataset , and investigating new potentially predictive features . In the slightly longer term , we plan to integrate the resulting system into a meeting assistant whose purpose is to automatically extract useful information from multi-party meetings . | Although the word "you" is frequently used and has several possible meanings, such as reference or generic, it is not well studied yet. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
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We explore the problem of resolving the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . First , we distinguish generic and referential uses , then we classify the referential uses as either plural or singular , and finally , for the latter cases , we identify the addressee . In our first set of experiments , the linguistic and visual features are derived from manual transcriptions and annotations , but in the second set , they are generated through entirely automatic means . Results show that a multimodal system is often preferable to a unimodal one . The English pronoun you is the second most frequent word in unrestricted conversation ( after I and right before it ) . 1 Despite this , with the exception of Gupta et al . ( 2007b ; 2007a ) , its resolution has received very little attention in the literature . This is perhaps not surprising since the vast amount of work on anaphora and reference resolution has focused on text or discourse -mediums where second-person deixis is perhaps not as prominent as it is in dialogue . For spoken dialogue pronoun resolution modules however , resolving you is an essential task that has an important impact on the capabilities of dialogue summarization systems . Besides being important for computational implementations , resolving you is also an interesting and challenging research problem . As for third person pronouns such as it , some uses of you are not strictly referential . These include discourse marker uses such as you know in example ( 1 ) , and generic uses like ( 2 ) , where you does not refer to the addressee as it does in ( 3 ) . ( 1 ) It 's not just , you know , noises like something hitting . ( 2 ) Often , you need to know specific button sequences to get certain functionalities done . ( 3 ) I think it 's good . You 've done a good review . However , unlike it , you is ambiguous between singular and plural interpretations -an issue that is particularly problematic in multi-party conversations . While you clearly has a plural referent in ( 4 ) , in ( 3 ) the number of its referent is ambiguous . 2(4 ) I do n't know if you guys have any questions . When an utterance contains a singular referential you , resolving the you amounts to identifying the individual to whom the utterance is addressed . This is trivial in two-person dialogue since the current listener is always the addressee , but in conversations with multiple participants , it is a complex problem where different kinds of linguistic and visual information play important roles ( Jovanovic , 2007 ) . One of the issues we investigate here is how this applies to the more concrete problem of resolving the second person pronoun you . We approach this issue as a three-step problem . Using the AMI Meeting Corpus ( McCowan et al . , 2005 ) of multi-party dialogues , we first discriminate between referential and generic uses of you . Then , within the referential uses , we distinguish between singular and plural , and finally , we resolve the singular referential instances by identifying the intended addressee . We use multimodal features : initially , we extract discourse features from manual transcriptions and use visual information derived from manual annotations , but then we move to a fully automatic approach , using 1-best transcriptions produced by an automatic speech recognizer ( ASR ) and visual features automatically extracted from raw video . In the next section of this paper , we give a brief overview of related work . We describe our data in Section 3 , and explain how we extract visual and linguistic features in Sections 4 and 5 respectively . Section 6 then presents our experiments with manual transcriptions and annotations , while Section 7 , those with automatically extracted information . We end with conclusions in Section 8 . We have investigated the automatic resolution of the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . We conducted a first set of experiments where our features were derived from manual transcriptions and annotations , and then a second set where they were generated by entirely automatic means . To our knowledge , this is the first attempt at tackling this problem using automatically extracted multimodal information . Our experiments showed that visual information can be highly predictive in resolving the addressee of singular referential uses of you . Visual features significantly improved the performance of both our manual and automatic systems , and the latter achieved an encouraging 75 % accuracy . We also found that our visual features had predictive power for distinguishing between generic and referential uses of you , and between referential singulars and plurals . Indeed , for the latter task , they significantly improved the manual system 's performance . The listeners ' gaze features were useful here : in our data set it was apparently the case that the speaker would often use the whiteboard / projector screen when addressing the group , thus drawing the listeners ' gaze in this direction . Future work will involve expanding our dataset , and investigating new potentially predictive features . In the slightly longer term , we plan to integrate the resulting system into a meeting assistant whose purpose is to automatically extract useful information from multi-party meetings .
| sobamchan/aclsum | 2 | full_paper |
D09-1115 | Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations , confusion networks have difficulty in handling more general situations in which several words are connected to another several words . Instead , we propose a lattice-based system combination model that allows for such phrase alignments and uses lattices to encode all candidate translations . Experiments show that our approach achieves significant improvements over the state-ofthe-art baseline system on Chinese-to-English translation test sets . System combination aims to find consensus translations among different machine translation systems . It has been proven that such consensus translations are usually better than the output of individual systems ( Frederking and Nirenburg , 1994 ) . In recent several years , the system combination methods based on confusion networks developed rapidly ( Bangalore et al . , 2001 ; Matusov et al . , 2006 ; Sim et al . , 2007 ; Rosti et al . , 2007a ; Rosti et al . , 2007b ; Rosti et al . , 2008 ; He et al . , 2008 ) , which show state-of-the-art performance in benchmarks . A confusion network consists of a sequence of sets of candidate words . Each candidate word is associated with a score . The optimal consensus translation can be obtained by selecting one word from each set to maximizing the overall score . To construct a confusion network , one first need to choose one of the hypotheses ( i.e. , candidate translations ) as the backbone ( also called " skeleton " in the literature ) and then decide the word alignments of other hypotheses to the backbone . Hypothesis alignment plays a crucial role in confusionnetwork-based system combination because it has a direct effect on selecting consensus translations . However , a confusion network is restricted in such a way that only 1-to-1 mappings are allowed in hypothesis alignment . This is not the fact even for word alignments between the same languages . It is more common that several words are connected to another several words . For example , " be capable of " and " be able to " have the same meaning . Although confusion-network-based approaches resort to inserting null words to alleviate this problem , they face the risk of producing degenerate translations such as " be capable to " and " be able of " . In this paper , we propose a new system combination method based on lattices . As a more general form of confusion network , a lattice is capable of describing arbitrary mappings in hypothesis alignment . In a lattice , each edge is associated with a sequence of words rather than a single word . Therefore , we select phrases instead of words in each candidate set and minimize the chance to produce unexpected translations such as " be capable to " . We compared our approach with the state-of-the-art confusion-network-based system ( He et al . , 2008 ) and achieved a significant absolute improvement of 1.23 BLEU points on the NIST 2005 Chinese-to-English test set and 0.93 BLEU point on the NIST 2008 Chinese-to-English test set . The alignment model plays an important role in system combination . Because of the expression limitation of confusion networks , only 1-to-1 mappings are employed in the confusion-network-based model . This paper proposes a lattice-based system combination model . As a general form of confusion networks , lattices can express n-to-n mappings . a confusion-network-based model processes words only . As a result , phrase pairs must be extracted before constructing a lattice . On NIST MT05 test set , the lattice-based system gave better results with an absolute improvement of 1.23 BLEU points over the confusionnetwork-based system ( He et al . , 2008 ) and 3.73 BLEU points over the best single system . On NIST MT08 test set , the lattice-based system outperformed the confusion-network-based system by 0.93 BLEU point and outperformed the best single system by 3.0 BLEU points . | System combination methods based on confusion networks only allow word level 1-to-1 mappings, and some workarounds cause another type of problem such as degeneration. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
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Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations , confusion networks have difficulty in handling more general situations in which several words are connected to another several words . Instead , we propose a lattice-based system combination model that allows for such phrase alignments and uses lattices to encode all candidate translations . Experiments show that our approach achieves significant improvements over the state-ofthe-art baseline system on Chinese-to-English translation test sets . System combination aims to find consensus translations among different machine translation systems . It has been proven that such consensus translations are usually better than the output of individual systems ( Frederking and Nirenburg , 1994 ) . In recent several years , the system combination methods based on confusion networks developed rapidly ( Bangalore et al . , 2001 ; Matusov et al . , 2006 ; Sim et al . , 2007 ; Rosti et al . , 2007a ; Rosti et al . , 2007b ; Rosti et al . , 2008 ; He et al . , 2008 ) , which show state-of-the-art performance in benchmarks . A confusion network consists of a sequence of sets of candidate words . Each candidate word is associated with a score . The optimal consensus translation can be obtained by selecting one word from each set to maximizing the overall score . To construct a confusion network , one first need to choose one of the hypotheses ( i.e. , candidate translations ) as the backbone ( also called " skeleton " in the literature ) and then decide the word alignments of other hypotheses to the backbone . Hypothesis alignment plays a crucial role in confusionnetwork-based system combination because it has a direct effect on selecting consensus translations . However , a confusion network is restricted in such a way that only 1-to-1 mappings are allowed in hypothesis alignment . This is not the fact even for word alignments between the same languages . It is more common that several words are connected to another several words . For example , " be capable of " and " be able to " have the same meaning . Although confusion-network-based approaches resort to inserting null words to alleviate this problem , they face the risk of producing degenerate translations such as " be capable to " and " be able of " . In this paper , we propose a new system combination method based on lattices . As a more general form of confusion network , a lattice is capable of describing arbitrary mappings in hypothesis alignment . In a lattice , each edge is associated with a sequence of words rather than a single word . Therefore , we select phrases instead of words in each candidate set and minimize the chance to produce unexpected translations such as " be capable to " . We compared our approach with the state-of-the-art confusion-network-based system ( He et al . , 2008 ) and achieved a significant absolute improvement of 1.23 BLEU points on the NIST 2005 Chinese-to-English test set and 0.93 BLEU point on the NIST 2008 Chinese-to-English test set . The alignment model plays an important role in system combination . Because of the expression limitation of confusion networks , only 1-to-1 mappings are employed in the confusion-network-based model . This paper proposes a lattice-based system combination model . As a general form of confusion networks , lattices can express n-to-n mappings . a confusion-network-based model processes words only . As a result , phrase pairs must be extracted before constructing a lattice . On NIST MT05 test set , the lattice-based system gave better results with an absolute improvement of 1.23 BLEU points over the confusionnetwork-based system ( He et al . , 2008 ) and 3.73 BLEU points over the best single system . On NIST MT08 test set , the lattice-based system outperformed the confusion-network-based system by 0.93 BLEU point and outperformed the best single system by 3.0 BLEU points .
| sobamchan/aclsum | 2 | full_paper |
N19-1233 | Generative Adversarial Networks ( GANs ) are a promising approach for text generation that , unlike traditional language models ( LM ) , does not suffer from the problem of " exposure bias " . However , A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric . In this work , we propose to approximate the distribution of text generated by a GAN , which permits evaluating them with traditional probability-based LM metrics . We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than stateof-the-art LMs . Our evaluation procedure promotes better understanding of the relation between GANs and LMs , and can accelerate progress in GAN-based text generation . Neural networks have revolutionized the field of text generation , in machine translation ( Sutskever et al . , 2014 ; Neubig , 2017 ; Luong et al . , 2015 ; Chen et al . , 2018 ) , summarization ( See et al . , 2017 ) , image captioning ( You et al . , 2016 ) and many other applications ( Goldberg , 2017 ) . Traditionally , text generation models are trained by going over a gold sequence of symbols ( characters or words ) from left-to-right , and maximizing the probability of the next symbol given the history , namely , a language modeling ( LM ) objective . A commonly discussed drawback of such LM-based text generation is exposure bias ( Ranzato et al . , 2015 ) : during training , the model predicts the next token conditioned on the ground truth history , while at test time prediction is based on predicted tokens , causing a train-test mismatch . Models trained in this manner often struggle to overcome previous prediction errors . Generative Adversarial Networks ( Goodfellow et al . , 2014 ) offer a solution for exposure bias . * The authors contributed equally Originally introduced for images , GANs leverage a discriminator , which is trained to discriminate between real images and generated images via an adversarial loss . In such a framework , the generator is not directly exposed to the ground truth data , but instead learns to imitate it using global feedback from the discriminator . This has led to several attempts to use GANs for text generation , with a generator using either a recurrent neural network ( RNN ) ( Yu et al . , 2017 ; Guo et al . , 2017 ; Press et al . , 2017 ; Rajeswar et al . , 2017 ) , or a Convolutional Neural Network ( CNN ) ( Gulrajani et al . , 2017 ; Rajeswar et al . , 2017 ) . However , evaluating GANs is more difficult than evaluating LMs . While in language modeling , evaluation is based on the log-probability of a model on held-out text , this can not be straightforwardly extended to GAN-based text generation , because the generator outputs discrete tokens , rather than a probability distribution . Currently , there is no single evaluation metric for GAN-based text generation , and existing metrics that are based on n-gram overlap are known to lack robustness and have low correlation with semantic coherence ( Semeniuta et al . , 2018 ) . In this paper , we propose a method for evaluating GANs with standard probability-based evaluation metrics . We show that the expected prediction of a GAN generator can be viewed as a LM , and suggest a simple Monte-Carlo method for approximating it . The approximated probability distribution can then be evaluated with standard LM metrics such as perplexity or Bits Per Character ( BPC ) . To empirically establish our claim , we implement our evaluation on several RNN-based GANs : ( Press et al . , 2017 ; Yu et al . , 2017 ; Guo et al . , 2017 ) . We find that all models have substantially lower BPC compared to state-of-the-art LMs . By directly comparing to LMs , we put in perspective the current performance of RNN-based GANs for text generation . Our results are also in line with recent concurrent work by Caccia et al . ( 2018 ) , who reached a similar conclusion by comparing the performance of textual GANs to that of LMs using metrics suggested for GAN evaluation . Our code is available at : http : //github.com / GuyTevet / SeqGAN-eval and http://github.com / GuyTevet/ rnn-gan-eval . We propose an evaluation procedure for text GANs that is based on approximating the GAN output distribution and using standard LM metrics . We provide a bound for the number of samples required for the approximation and empirically show in practice as few as 2000 samples per time-step suffice . We evaluate character-based GAN models using our procedure , and show their performance is substantially lower than state-of-the-art LM . We hope our simple evaluation method leads to progress in GAN-based text generation by shedding light on the quality of such models . | They propose a way to approximate distributions from GAN-based models' outputs so that they can be evaluated as standard language models. | approach | coverage_first | What did the authors do?
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Generative Adversarial Networks ( GANs ) are a promising approach for text generation that , unlike traditional language models ( LM ) , does not suffer from the problem of " exposure bias " . However , A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric . In this work , we propose to approximate the distribution of text generated by a GAN , which permits evaluating them with traditional probability-based LM metrics . We apply our approximation procedure on several GAN-based models and show that they currently perform substantially worse than stateof-the-art LMs . Our evaluation procedure promotes better understanding of the relation between GANs and LMs , and can accelerate progress in GAN-based text generation . Neural networks have revolutionized the field of text generation , in machine translation ( Sutskever et al . , 2014 ; Neubig , 2017 ; Luong et al . , 2015 ; Chen et al . , 2018 ) , summarization ( See et al . , 2017 ) , image captioning ( You et al . , 2016 ) and many other applications ( Goldberg , 2017 ) . Traditionally , text generation models are trained by going over a gold sequence of symbols ( characters or words ) from left-to-right , and maximizing the probability of the next symbol given the history , namely , a language modeling ( LM ) objective . A commonly discussed drawback of such LM-based text generation is exposure bias ( Ranzato et al . , 2015 ) : during training , the model predicts the next token conditioned on the ground truth history , while at test time prediction is based on predicted tokens , causing a train-test mismatch . Models trained in this manner often struggle to overcome previous prediction errors . Generative Adversarial Networks ( Goodfellow et al . , 2014 ) offer a solution for exposure bias . * The authors contributed equally Originally introduced for images , GANs leverage a discriminator , which is trained to discriminate between real images and generated images via an adversarial loss . In such a framework , the generator is not directly exposed to the ground truth data , but instead learns to imitate it using global feedback from the discriminator . This has led to several attempts to use GANs for text generation , with a generator using either a recurrent neural network ( RNN ) ( Yu et al . , 2017 ; Guo et al . , 2017 ; Press et al . , 2017 ; Rajeswar et al . , 2017 ) , or a Convolutional Neural Network ( CNN ) ( Gulrajani et al . , 2017 ; Rajeswar et al . , 2017 ) . However , evaluating GANs is more difficult than evaluating LMs . While in language modeling , evaluation is based on the log-probability of a model on held-out text , this can not be straightforwardly extended to GAN-based text generation , because the generator outputs discrete tokens , rather than a probability distribution . Currently , there is no single evaluation metric for GAN-based text generation , and existing metrics that are based on n-gram overlap are known to lack robustness and have low correlation with semantic coherence ( Semeniuta et al . , 2018 ) . In this paper , we propose a method for evaluating GANs with standard probability-based evaluation metrics . We show that the expected prediction of a GAN generator can be viewed as a LM , and suggest a simple Monte-Carlo method for approximating it . The approximated probability distribution can then be evaluated with standard LM metrics such as perplexity or Bits Per Character ( BPC ) . To empirically establish our claim , we implement our evaluation on several RNN-based GANs : ( Press et al . , 2017 ; Yu et al . , 2017 ; Guo et al . , 2017 ) . We find that all models have substantially lower BPC compared to state-of-the-art LMs . By directly comparing to LMs , we put in perspective the current performance of RNN-based GANs for text generation . Our results are also in line with recent concurrent work by Caccia et al . ( 2018 ) , who reached a similar conclusion by comparing the performance of textual GANs to that of LMs using metrics suggested for GAN evaluation . Our code is available at : http : //github.com / GuyTevet / SeqGAN-eval and http://github.com / GuyTevet/ rnn-gan-eval . We propose an evaluation procedure for text GANs that is based on approximating the GAN output distribution and using standard LM metrics . We provide a bound for the number of samples required for the approximation and empirically show in practice as few as 2000 samples per time-step suffice . We evaluate character-based GAN models using our procedure , and show their performance is substantially lower than state-of-the-art LM . We hope our simple evaluation method leads to progress in GAN-based text generation by shedding light on the quality of such models .
| sobamchan/aclsum | 2 | full_paper |
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