Emotion Recognition in Conversations (ERC) has been gaining increasing importance as conversational agents become more and more common. Recognizing emotions is key for effective communication, being a crucial component in the development of effective and empathetic conversational agents. Knowledge and understanding of the conversational context are extremely valuable for identifying the emotions of the interlocutor. We thus approach Emotion Recognition in Conversations leveraging the conversational context, i.e., taking into attention previous conversational turns. The usual approach to model the conversational context has been to produce context-independent representations of each utterance and subsequently perform contextual modeling of these. Here we propose context-dependent embedding representations of each utterance by leveraging the contextual representational power of pre-trained transformer language models. In our approach, we feed the conversational context appended to the utterance to be classified as input to the RoBERTa encoder, to which we append a simple classification module, thus discarding the need to deal with context after obtaining the embeddings since these constitute already an efficient representation of such context. We also investigate how the number of introduced conversational turns influences our model performance. The effectiveness of our approach is validated on the widely used open-domain DailyDialog dataset and on the task-oriented EmoWOZ dataset, for which we attain state-of-the-art results, surpassing ERC models also resorting to RoBERTa but with more complex classification modules, indicating that our context-dependent embedding utterance representation approach with a simple classification model can be more effective than context-independent utterance representation approaches with more complex classification modules.

翻译：对话情感识别（ERC）随着对话代理的普及日益受到重视。情感识别是实现有效沟通的关键，也是开发高效且富有同理心的对话代理的核心组成部分。对话上下文的认知与理解对于识别对话者的情感极具价值。为此，我们利用对话上下文（即关注先前的对话轮次）进行对话情感识别。传统建模对话上下文的方法是为每个语句生成上下文无关表示，随后对这些表示进行上下文建模。本文提出通过利用预训练Transformer语言模型的上下文表示能力，为每个语句生成上下文相关的嵌入表示。具体而言，我们将待分类语句附加对话上下文后输入RoBERTa编码器，并在其后添加简单的分类模块，从而无需在获取嵌入后再处理上下文，因为这些嵌入本身已构成对上下文的有效表示。我们还探究了引入的对话轮次数对模型性能的影响。本方法的有效性在广泛使用的开放域数据集DailyDialog和任务导向数据集EmoWOZ上得到验证，我们在这些数据集上取得了最先进的结果，超越了同样使用RoBERTa但配备更复杂分类模块的ERC模型。这表明，我们的上下文相关嵌入语句表示方法配合简单分类模型，比采用更复杂分类模块的上下文无关语句表示方法更具优势。