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A Token Classification-Based Attention Model for Extracting Multiple Emotion-Cause Pairs in Conversations.

Soyeop Yoo1, Okran Jeong1

  • 1School of Computing, Gachon University, 1342 Seongnam-daero, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|March 30, 2023
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Summary

This study introduces a new method for extracting multiple emotion-cause pairs from conversations. The novel model efficiently identifies emotions and their causes in a single step, improving upon existing emotion-cause pair extraction techniques.

Keywords:
conversational AIemotion–cause extractionemotion–cause pair extractionpre-trained language modeltoken classification

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Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Affective Computing

Background:

  • Conversational AI requires understanding emotions and their origins.
  • Existing emotion-cause pair extraction (ECPE) methods are often multi-step or extract only single pairs.
  • Accurate ECPE is crucial for nuanced conversational analysis.

Purpose of the Study:

  • To develop a novel, single-model methodology for simultaneous extraction of multiple emotion-cause pairs from conversations.
  • To address limitations of existing ECPE approaches in handling multiple pairs and multi-step processes.

Main Methods:

  • Proposed a token-classification-based model for ECPE.
  • Utilized the BIO (beginning-inside-outside) tagging scheme for efficient extraction.
  • Developed a single-model approach for simultaneous multi-pair extraction.

Main Results:

  • The proposed model achieved state-of-the-art performance on the RECCON benchmark dataset.
  • Demonstrated efficient extraction of multiple emotion-cause pairs within conversations.
  • Outperformed existing ECPE methods in comparative experiments.

Conclusions:

  • The novel methodology effectively extracts multiple emotion-cause pairs simultaneously using a single model.
  • The BIO tagging scheme enhances efficiency in conversational ECPE.
  • This approach represents a significant advancement in automated emotion understanding in dialogues.