LastResort 在 SemEval-2024 任务 3：将多模态情绪原因对抽取作为序列标注任务进行探索
LastResort at SemEval-2024 Task 3: Exploring Multimodal Emotion Cause Pair Extraction as Sequence Labelling Task
AI总结：
本文将多模态情绪原因对抽取建模为话语标注与序列标注任务，结合不同编码器、BiLSTM 上下文建模和 CRF 层，在 SemEval-2024 任务 3 官方排行榜上以 0.1759 的 F1 分数排名第 8。
摘要：
对话是人类交流中最自然的形式，其中每一句话语都可能表达多种可能的情绪。尽管在文本情绪检测方面已经开展了大量工作，但在寻找这些情绪的原因方面，尤其是在多模态环境下，所做的工作相对较少。SemEval 2024 引入了对话中的多模态情绪原因分析任务，旨在抽取涉及多种模态（文本、音频和视觉模态）的对话中单个话语所反映的情绪，以及作为该情绪原因的相应话语。在本文中，我们提出了将该任务作为话语标注和序列标注问题来处理的模型，并对这些模型进行了比较研究，包括使用不同编码器的基线模型、使用 BiLSTM 添加对话上下文信息，以及最后添加 CRF 层以尝试更有效地建模相邻话语之间的相互依赖关系。在该任务的官方排行榜上，我们的架构排名第 8，在排行榜上取得了 0.1759 的 F1 分数。
英文摘要：
Conversation is the most natural form of human communication, where each utterance can range over a variety of possible emotions. While significant work has been done towards the detection of emotions in text, relatively little work has been done towards finding the cause of the said emotions, especially in multimodal settings. SemEval 2024 introduces the task of Multimodal Emotion Cause Analysis in Conversations, which aims to extract emotions reflected in individual utterances in a conversation involving multiple modalities (textual, audio, and visual modalities) along with the corresponding utterances that were the cause for the emotion. In this paper, we propose models that tackle this task as an utterance labeling and a sequence labeling problem and perform a comparative study of these models, involving baselines using different encoders, using BiLSTM for adding contextual information of the conversation, and finally adding a CRF layer to try to model the inter-dependencies between adjacent utterances more effectively. In the official leaderboard for the task, our architecture was ranked 8th, achieving an F1-score of 0.1759 on the leaderboard.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
