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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 80-88    DOI: 10.11925/infotech.2096-3467.2021.0347
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Analyzing Implicit Discourse Relation with Single Classifier and Multi-Task Network
Wang Hong,Shu Zhan,Gao Yinquan,Tian Wenhong()
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Yangtze Delta Region Institute of University of Electronic Science and Technology of China, Huzhou 313001, China
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Abstract  

[Objective] This paper proposes a new method to identify implicit discourse relations based on a single classifier and multi-task learning model. [Methods] First,we modeled the implicit and explicit discourse relationships with the multi-task learning method. Then, we converted the four classification problems to two and trained the single classifier. [Results] We examined our new method with the HIT-CDTB data set. For the corpus with extended and parallel relations, the F1 values reached 0.94 and 0.81 respectively, which were significantly improved with four inter-sentence relations. [Limitations] The performance of our model could be improved with more distributed and expanded datasets. [Conclusions] The proposed method yields the best results with the HIT-CDTB data set. Deleting connectives will add noise to the training set and negatively affect the model’s performance.

Key wordsSingle Classifier      Multi-Task Network      Implicit dDiscourse Relation     
Received: 08 April 2021      Published: 26 August 2021
ZTFLH:  TP391  
Fund:Key Research and Development Program of Ministry of Science and Technology(2018AAA0103203)
Corresponding Authors: Tian Wenhong,ORCID:0000-0002-5551-9796     E-mail: tian_wenhong@uestc.edu.cn

Cite this article:

Wang Hong, Shu Zhan, Gao Yinquan, Tian Wenhong. Analyzing Implicit Discourse Relation with Single Classifier and Multi-Task Network. Data Analysis and Knowledge Discovery, 2021, 5(11): 80-88.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0347     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/80

f x = w 1 x 2 + w 2 x + b Model Loss
">
f x = w 1 x 2 + w 2 x + b Model Loss
f x = w 1 x + b Model Loss
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f x = w 1 x + b Model Loss
Single Classifier Learning Structure
Multi-task Learning Structure
关系 隐式 显式
时序关系 178 672
因果关系 1 014 1 465
条件关系 39 632
对比关系 461 1 923
扩展关系 12 055 2 183
并列关系 1 288 993
Corpus Statistics
关系 多任务+单分类器 SVM[1] SVM[20]
P R F1 P R F1 P R F1
因果 0.50 0.51 0.50 0.46 0.22 0.30 0.59 0.06 0.11
对比 0.39 0.38 0.38 0.77 0.09 0.16 0.33 0.01 0.02
扩展 0.94 0.94 0.94 0.63 0.84 0.72 0.65 0.93 0.77
并列 0.82 0.81 0.81 0.33 0.53 0.41 0.65 0.54 0.59
Recognition Results of Implicit Inter Sentence Relationships
关系 多任务+单分类器 RNN-Att[8] 多任务[10] BiGRU
P R F1 P R F1 P R F1 P R F1
因果 0.50 0.51 0.50 0.27 0.12 0.17 0.43 0.34 0.38 0.31 0.26 0.28
对比 0.39 0.38 0.38 0.17 0.04 0.07 0.19 0.14 0.16 0.06 0.07 0.06
扩展 0.94 0.94 0.94 0.86 0.94 0.90 0.76 0.80 0.78 0.90 0.92 0.91
并列 0.82 0.81 0.81 0.67 0.58 0.62 0.83 0.77 0.80 0.73 0.59 0.65
Comparative Experiment of RNN-Att Model
Multi-task Network Structure
关系 多任务BiLSTM 多任务+单分类器
P R F1 P R F1
因果 0.24 0.17 0.20 0.50 0.51 0.50
对比 0.29 0.10 0.15 0.39 0.38 0.38
扩展 0.89 0.95 0.92 0.94 0.94 0.94
并列 0.87 0.68 0.76 0.82 0.81 0.81
Single Classifier Comparison Experiment 1
关系 多任务BiLSTM单分类器
P R F1
因果 0.35 0.15 0.21
对比 0.33 0.10 0.15
扩展 0.89 0.64 0.74
并列 0.88 0.49 0.63
Single Classifier Comparison Experiment 2
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