%0 Conference Proceedings %T Conversational Emotion-Cause Pair Extraction with Guided Mixture of Experts %A Jeong, DongJin %A Bak, JinYeong %Y Vlachos, Andreas %Y Augenstein, Isabelle %S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics %D 2023 %8 May %I Association for Computational Linguistics %C Dubrovnik, Croatia %F jeong-bak-2023-conversational %X 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. %R 10.18653/v1/2023.eacl-main.240 %U https://aclanthology.org/2023.eacl-main.240 %U https://doi.org/10.18653/v1/2023.eacl-main.240 %P 3288-3298