论文标题
多模式知识的多次降低和情感知识的变压器用于多模式对话情感识别
A Multibias-mitigated and Sentiment Knowledge Enriched Transformer for Debiasing in Multimodal Conversational Emotion Recognition
论文作者
论文摘要
对话中的多模式情感识别（MERC）是自然语言处理（NLP）的一个积极研究主题，该主题旨在在多种方式，e，g。，自然语言和面部手势的交流中预测人类的情绪状态。无数的隐式偏见和神觉符合人的语言和对话，导致了当前数据驱动的MERC方法是否会产生偏见错误的问题。例如，这种方法可能比男性提供更高的情感分数。此外，现有的DEBIA模型主要集中于性别或种族，在这种性别或种族中，缓解多重次数仍然是MERC中未开发的任务。在这项工作中，我们迈出了第一步，通过提出一系列方法来缓解文本话语（即性别，年龄，年龄，种族，宗教和LGBTQ+）和视觉表现（即性别和年龄）的五种典型偏见来解决这些问题，然后是多伯族和情感知识知识知识知识丰富的生物模式变成（Mmmodal）。全面的实验结果表明了所提出的模型的有效性，并证明了Debias操作对MERC的分类性能有很大的影响。我们希望我们的研究将有益于MERC和相关情绪研究中缓解偏见的发展。
Multimodal emotion recognition in conversations (mERC) is an active research topic in natural language processing (NLP), which aims to predict human's emotional states in communications of multiple modalities, e,g., natural language and facial gestures. Innumerable implicit prejudices and preconceptions fill human language and conversations, leading to the question of whether the current data-driven mERC approaches produce a biased error. For example, such approaches may offer higher emotional scores on the utterances by females than males. In addition, the existing debias models mainly focus on gender or race, where multibias mitigation is still an unexplored task in mERC. In this work, we take the first step to solve these issues by proposing a series of approaches to mitigate five typical kinds of bias in textual utterances (i.e., gender, age, race, religion and LGBTQ+) and visual representations (i.e, gender and age), followed by a Multibias-Mitigated and sentiment Knowledge Enriched bi-modal Transformer (MMKET). Comprehensive experimental results show the effectiveness of the proposed model and prove that the debias operation has a great impact on the classification performance for mERC. We hope our study will benefit the development of bias mitigation in mERC and related emotion studies.
