TY - GEN
T1 - Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts
AU - Jeong, Dong Jin
AU - Bak, Jin Yeong
N1 - Publisher Copyright:
© 2023 Association for Computational Linguistics.
PY - 2023
Y1 - 2023
N2 - Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents. ECPE is an important task for developing human-like responses. However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues. To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information). PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement. We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles. We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion. For reproducing the results, we make available all our code and data.
AB - Emotion-Cause Pair Extraction (ECPE) task aims to pair all emotions and corresponding causes in documents. ECPE is an important task for developing human-like responses. However, previous ECPE research is conducted based on news articles, which has different characteristics compared to dialogues. To address this issue, we propose a Pair-Relationship Guided Mixture-of-Experts (PRG-MoE) model, which considers dialogue features (e.g., speaker information). PRG-MoE automatically learns relationship between utterances and advises a gating network to incorporate dialogue features in the evaluation, yielding substantial performance improvement. We employ a new ECPE dataset, which is an English dialogue dataset, with more emotion-cause pairs in documents than news articles. We also propose Cause Type Classification that classifies emotion-cause pairs according to the types of the cause of a detected emotion. For reproducing the results, we make available all our code and data.
UR - https://www.scopus.com/pages/publications/85159850162
U2 - 10.18653/v1/2023.eacl-main.240
DO - 10.18653/v1/2023.eacl-main.240
M3 - Conference contribution
AN - SCOPUS:85159850162
T3 - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
SP - 3280
EP - 3290
BT - EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023
Y2 - 2 May 2023 through 6 May 2023
ER -