M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations

• URL: http://arxiv.org/abs/2203.16799v4
• Date: Fri, 31 Mar 2023 13:25:05 GMT
• Title: M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations
• Authors: Sreyan Ghosh and S Ramaneswaran and Utkarsh Tyagi and Harshvardhan Srivastava and Samden Lepcha and S Sakshi and Dinesh Manocha
• Abstract summary: Emotion recognition in conversations (ERC) is an emerging field of study, where the primary task is to identify the emotion behind each utterance in a conversation. We present Multilingual MELD (M-MELD), where we extend the Multimodal EmotionLines dataset (MELD) to 4 other languages beyond English. We also propose a novel architecture, DiscLSTM, that uses both sequential and conversational discourse context in a conversational dialogue for ERC.
• Score: 42.512843360461886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Expression of emotions is a crucial part of daily human communication. Emotion recognition in conversations (ERC) is an emerging field of study, where the primary task is to identify the emotion behind each utterance in a conversation. Though a lot of work has been done on ERC in the past, these works only focus on ERC in the English language, thereby ignoring any other languages. In this paper, we present Multilingual MELD (M-MELD), where we extend the Multimodal EmotionLines Dataset (MELD) \cite{poria2018meld} to 4 other languages beyond English, namely Greek, Polish, French, and Spanish. Beyond just establishing strong baselines for all of these 4 languages, we also propose a novel architecture, DiscLSTM, that uses both sequential and conversational discourse context in a conversational dialogue for ERC. Our proposed approach is computationally efficient, can transfer across languages using just a cross-lingual encoder, and achieves better performance than most uni-modal text approaches in the literature on both MELD and M-MELD. We make our data and code publicly on GitHub.

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