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Deep Enhanced Representation for Implicit Discourse Relation Recognition [article]

Hongxiao Bai, Hai Zhao
2018 arXiv   pre-print
Implicit discourse relation recognition is a challenging task as the relation prediction without explicit connectives in discourse parsing needs understanding of text spans and cannot be easily derived  ...  In this paper, we propose a model augmented with different grained text representations, including character, subword, word, sentence, and sentence pair levels.  ...  In this work, we focus on improving the learned representations of sentence pairs to address the implicit discourse relation recognition.  ... 
arXiv:1807.05154v1 fatcat:usflxkgljjbnnbzgy2np66nrda

Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings

Lianhui Qin, Zhisong Zhang, Hai Zhao
2016 International Conference on Computational Linguistics  
For the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem.  ...  Neural models provide a solution with distributed representations, which could encode the latent semantic information, and are suitable for recognizing semantic relations between argument pairs.  ...  resulting in high perplexities for discourse relation recognition.  ... 
dblp:conf/coling/QinZZ16 fatcat:f7zkmz3l4ng5jmjsiduo4u3x5i

A Survey of Implicit Discourse Relation Recognition [article]

Wei Xiang, Bang Wang
2022 arXiv   pre-print
The task of implicit discourse relation recognition (IDRR) is to detect implicit relation and classify its sense between two text segments without a connective.  ...  Finally, we discuss future research directions for discourse relation analysis.  ...  IMPLICIT DISCOURSE RELATION RECOGNITION BASED ON DEEP LEARNING The aforementioned ML approaches heavily rely on various hand-crafted features to construct argument representation for discourse relation  ... 
arXiv:2203.02982v1 fatcat:ubublxw2fnfdpexgw4jslj76tm

Learning better discourse representation for implicit discourse relation recognition via attention networks

Biao Zhang, Deyi Xiong, Jinsong Su, Min Zhang
2018 Neurocomputing  
It is able to retrieve a deep semantic meaning representation for the discourse from the memory.  ...  Using the surface and semantic representations as input, SeMDER finally predicts implicit discourse relations via a neural recognizer.  ...  First, we propose a neural network architecture for implicit DRR with an encoded semantic memory that enhances representations of arguments.  ... 
doi:10.1016/j.neucom.2017.09.074 fatcat:57tpohekfzgz7odqpzuc4mxnsu

Learning Contextually Informed Representations for Linear-Time Discourse Parsing

Yang Liu, Mirella Lapata
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
We present a novel multi-task attentionbased neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attentionbased  ...  neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora.  ...  Acknowledgments This work is supported by grants from Science and Technology Commission of Shanghai Municipality (14DZ2260800 and 15ZR1410700), Shanghai Collaborative Innovation Center of Trustworthy Software for  ... 
doi:10.18653/v1/d17-1133 dblp:conf/emnlp/LiuL17 fatcat:u7eizua2nrezlirzzsv74ut3qm

Memorizing All for Implicit Discourse Relation Recognition [article]

Hongxiao Bai, Hai Zhao, Junhan Zhao
2019 arXiv   pre-print
Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clue from explicit connectives.  ...  implicit discourse relation recognizer.  ...  Conclusion In this paper, we propose a novel memory component to enhance state-of-the-art implicit discourse relation recognition model.  ... 
arXiv:1908.11317v1 fatcat:bxl4bgebwfae3gnyb6tp4yh2qq

Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition

Fengyu Guo, Ruifang He, Jianwu Dang, Jian Wang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Recognizing implicit discourse relation is a challenging task in discourse analysis, which aims to understand and infer the latent relations between two discourse arguments, such as temporal, comparison  ...  Most of the present models largely focus on learning-based methods that utilize only intra-sentence textual information to identify discourse relations, ignoring the wider contexts beyond the discourse  ...  Acknowledgments We thank the anonymous reviewers for their valuable feedback. Our work is supported by the National Natural Science  ... 
doi:10.1609/aaai.v34i05.6287 fatcat:smjklclp2ncibbmqashawugnfe

Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition

Biao Zhang, Jinsong Su, Deyi Xiong, Yaojie Lu, Hong Duan, Junfeng Yao
2015 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing  
In this paper, we propose a Shallow Convolutional Neural Network (SCNN) for implicit discourse relation recognition, which contains only one hidden layer but is effective in relation recognition.  ...  Implicit discourse relation recognition remains a serious challenge due to the absence of discourse connectives.  ...  We thank the anonymous reviewers for their insightful comments. We are also grateful to Kaixu Zhang for his valuable suggestions.  ... 
doi:10.18653/v1/d15-1266 dblp:conf/emnlp/ZhangSXLDY15 fatcat:wzocicuwzza23hejrxhocsuhke

Implicit Discourse Relation Identification for Open-domain Dialogues

Mingyu Derek Ma, Kevin Bowden, Jiaqi Wu, Wen Cui, Marilyn Walker
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Discourse relation identification has been an active area of research for many years, and the challenge of identifying implicit relations remains largely an unsolved task, especially in the context of  ...  We firstly propose a method to automatically extract the implicit discourse relation argument pairs and labels from a dataset of dialogic turns, resulting in a novel corpus of discourse relation pairs;  ...  relation recognition possible.  ... 
doi:10.18653/v1/p19-1065 dblp:conf/acl/MaBWCW19 fatcat:exvas72csvgpvkroek2kmy53l4

Leveraging Hierarchical Deep Semantics to Classify Implicit Discourse Relations via Mutual Learning Method [chapter]

Xiaohan She, Ping Jian, Pengcheng Zhang, Heyan Huang
2016 Lecture Notes in Computer Science  
This paper presents a mutual learning method using hierarchical deep semantics for the classification of implicit discourse relations in English.  ...  During the training process, the predicted target of the model which is the probability of the discourse relation type, and the distributed representation of semantic components are learnt jointly and  ...  Acknowledgment The authors would like to thank the organizers of NLPCC-ICCPOL 2016 and the reviewers for their helpful suggestions.  ... 
doi:10.1007/978-3-319-50496-4_29 fatcat:ngnd3brc2vfbvmcbdimpv7xwjq

Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning

Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, Xiangang Li
2018 International Conference on Computational Linguistics  
Implicit discourse relation recognition aims to understand and annotate the latent relations between two discourse arguments, such as temporal, comparison, etc.  ...  In this paper, we propose a novel Neural Tensor Network framework with Interactive Attention and Sparse Learning (TIASL) for implicit discourse relation recognition. (1) We mine the most correlated word  ...  We also thank the anonymous reviewers for their valuable comments.  ... 
dblp:conf/coling/GuoHJDWL18 fatcat:5jebsoaueneitj4p7agv7dlf4u

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, Eric Xing
2017 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition  ...  features for accurate classification.  ...  Related Work Implicit Discourse Relation Recognition There has been a surge of interest in implicit discourse parsing since the release of PDTB (Prasad et al., 2008) , the first large discourse corpus  ... 
doi:10.18653/v1/p17-1093 dblp:conf/acl/QinZZHX17 fatcat:rkoz46lqtbhfjmh4tjedpxmhqy

Tree framework with BERT word embedding for the recognition of Chinese implicit discourse relations

Dan Jiang, Jin He
2020 IEEE Access  
IMPLICIT DISCOURSE RELATION RECOGNITION IN CHINESE Implicit DRR has been widely studied in recent years, and can be applied to multiple fields.  ...  INTRODUCTION D ISCOURSE relation recognition (DRR) aims to identify the semantic relation of two sentences or clauses.  ... 
doi:10.1109/access.2020.3019500 fatcat:kbknwhehgrhkrh6jf543wcqvr4

Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives

Yudai Kishimoto, Yugo Murawaki, Sadao Kurohashi
2020 International Conference on Language Resources and Evaluation  
relations to implicit discourse relations, we add a task named explicit connective prediction at the additional pre-training step. (3) To exploit implicit connectives given by treebank annotators, we  ...  However, there have been few reports on its application to implicit discourse relation classification, and it is not clear how BERT is best adapted to the task.  ...  For this reason, the recognition of implicit relations is the bottleneck of discourse relation classification (Xue et al., 2016; Dai and Huang, 2019) .  ... 
dblp:conf/lrec/KishimotoMK20 fatcat:a4b64a4b5rcsnidwtbwmwqmfcy

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification [article]

Xin Liu, Jiefu Ou, Yangqiu Song, Xin Jiang
2020 arXiv   pre-print
Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at  ...  We also analyze the effectiveness of different modules in the implicit discourse relation classification task and demonstrate how different levels of representation learning can affect the results.  ...  Conclusion We present a novel model, BMGF-RoBERTa, that combines representation, matching, and fusion modules for implicit discourse relation classification.  ... 
arXiv:2004.12617v2 fatcat:msw4i3es5rcjfo5m5jqbtj36me
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