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.

翻译：情感表达是人类日常交流的重要组成部分。对话情感识别（ERC）是一个新兴的研究领域，其主要任务是识别对话中每个话语背后的情感。尽管过去在ERC方面已有大量研究工作，但这些工作仅关注英语的ERC，从而忽略了其他语言。在本文中，我们提出了多语言MELD（M-MELD），将多模态情感线数据集（MELD）\cite{poria2018meld}从英语扩展到其他四种语言，即希腊语、波兰语、法语和西班牙语。除了为所有这四种语言建立强基线外，我们还提出了一种新颖的架构DiscLSTM，该架构在对话中同时利用序列和对话篇章上下文进行ERC。我们提出的方法计算效率高，仅使用跨语言编码器即可实现跨语言迁移，并且在MELD和M-MELD上的性能均优于文献中大多数单模态文本方法。我们在GitHub上公开了数据和代码。