A Reinforcement Learning Based Two-Stage Model for Emotion Cause Pair Extraction

Xinhong CHEN, Qing LI, Zongxi LI, Haoran XIE, Fu Lee WANG, Jianping WANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Recently, many efforts have been devoted to promoting the Emotion-Cause Pair Extraction (ECPE) task, as jointly extracting emotions and their causes is considered more helpful than only identifying the emotions in many applications. Among the existing efforts, end-to-end approaches are getting popular as the main trend, while others like pipeline models have been overlooked due to their potential issues of cascading errors. Nevertheless, the advantages of the pipeline models, such as logically dividing a complicated task into multiple easier subtasks, are underestimated and not well exploited. Moreover, the existing end-to-end approaches fail to capture the implicit co-occurrence or exclusion patterns between multiple pairs of emotions and causes since they are extracted independently. In view of these limitations, we propose a novel two-stage model to address the ECPE task and incorporate reinforcement learning (RL) to tackle the cascading error issue. In particular, our two-stage model first detects emotion clauses and then recognizes cause clauses for each detected emotion clause sequentially. By representing the error of each decision as an explicit reward, our model clearly knows how the error at each stage affects the final performance, hence the model can adjust itself for better performance. Furthermore, the sequential prediction enables our model to use the results achieved in the previous stages as auxiliary information in the subsequent stages. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed two-stage model, and the ablation comparison shows the promising effect of reducing cascading errors by incorporating RL.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Affective Computing
DOIs
Publication statusE-pub ahead of print - 1 Nov 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Causality Mining
  • Data mining
  • Emotion Causal Relationship
  • Emotion Understanding
  • Emotion recognition
  • Information Extraction
  • Pipelines
  • Predictive models
  • Reinforcement Learning
  • Reinforcement learning
  • Task analysis
  • Training

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