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Expression of emotions is a crucial part of daily human communication.Emotion recognition in conversations (ERC) is an emerging field of study, wherethe primary task is to identify the emotion behind each utterance in aconversation. Though a lot of work has been done on ERC in the past, theseworks only focus on ERC in the English language, thereby ignoring any otherlanguages. In this paper, we present Multilingual MELD (M-MELD), where weextend the Multimodal EmotionLines Dataset (MELD) \cite{poria2018meld} to 4other languages beyond English, namely Greek, Polish, French, and Spanish.Beyond just establishing strong baselines for all of these 4 languages, we alsopropose a novel architecture, DiscLSTM, that uses both sequential andconversational discourse context in a conversational dialogue for ERC. Ourproposed approach is computationally efficient, can transfer across languagesusing just a cross-lingual encoder, and achieves better performance than mostuni-modal text approaches in the literature on both MELD and M-MELD. We makeour data and code publicly on GitHub.

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