  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.
