AIMA at SemEval-2024 Task 10: History-Based Emotion Recognition in Hindi-English Code-Mixed Conversations

TL;DR

This paper presents a system for emotion recognition in Hindi-English code-mixed conversations, using context-aware models and translation techniques to improve performance in the SemEval-2024 Task 10.

Contribution

It introduces a novel approach combining context-aware modeling and translation pipelines specifically for code-mixed emotion recognition.

Findings

Ensembled models outperform baselines

Contextual information improves emotion detection accuracy

Translation pipeline aids in processing code-mixed data

Abstract

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.