用《老友记》数据构建轻量级多模态情感识别基线，验证融合效果。
A Baseline Multimodal Approach to Emotion Recognition in Conversations
- 文本用Transformer分类器，语音用自监督模型，后期简单融合。
- 多模态融合在多数情况下优于单一模态，提升约2.3%准确率。
- 适合想快速复现或对比新方法的研究者参考。
我们基于《老友记》构建的SemEval-2024任务3数据集，提出一种轻量级多模态情感识别基线方法。该方法结合（i）基于Transformer的文本分类器和（ii）自监督语音表示模型，采用简单的后期融合集成策略。报告旨在提供一个可访问的参考实现，而非提出新的最先进方法。在有限训练协议下，我们汇报了基线设置及实证结果，重点分析了多模态融合相对于单模态模型的增益情况。本预印本旨在提高透明度，并为未来更严格的比较提供基准支持。
原文摘要 · Abstract (English)
We present a lightweight multimodal baseline for emotion recognition in conversations using the SemEval-2024 Task 3 dataset built from the sitcom Friends. The goal of this report is not to propose a novel state-of-the-art method, but to document an accessible reference implementation that combines (i) a transformer-based text classifier and (ii) a self-supervised speech representation model, with a simple late-fusion ensemble. We report the baseline setup and empirical results obtained under a limited training protocol, highlighting when multimodal fusion improves over unimodal models. This preprint is provided for transparency and to support future, more rigorous comparisons.
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