针对印英混用对话的情感识别，提出融合前后上下文的多模型集成方案。
AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations
- 利用前文与后文上下文及对话序列信息建模，增强对混用语境的理解。
- 通过英译印英语料管道处理代码混用数据，提升模型适配性。
- 多模型集成策略在任务中表现领先，适合跨语言情感分析研究者。
本研究针对SemEval 2024任务10的子任务1——印英混用对话中的情感识别（ERC）问题，提出一种解决方案。由于现有模型多基于单语数据训练，在混用语境下表现不佳，本文设计了四类不同架构的基线模型，均采用强大的预训练编码器提取输入特征，并引入当前话语的前后文及对话序列信息。为更好处理混用文本，我们构建了一个从印英混合语到英文的翻译流水线。最终通过集成所有模型，得到一个性能超越所有基线的统一模型。
原文摘要 · Abstract (English)
In this study, we introduce a solution to the SemEval 2024 Task 10 on subtask 1, dedicated to Emotion Recognition in Conversation (ERC) in code-mixed Hindi-English conversations. ERC in code-mixed conversations presents unique challenges, as existing models are typically trained on monolingual datasets and may not perform well on code-mixed data. To address this, we propose a series of models that incorporate both the previous and future context of the current utterance, as well as the sequential information of the conversation. To facilitate the processing of code-mixed data, we developed a Hinglish-to-English translation pipeline to translate the code-mixed conversations into English. We designed four different base models, each utilizing powerful pre-trained encoders to extract features from the input but with varying architectures. By ensembling all of these models, we developed a final model that outperforms all other baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。