  Multimodal Sentiment Analysis (MSA) aims to understand human intentions by
integrating emotion-related clues from diverse modalities, such as visual,
language, and audio. Unfortunately, the current MSA task invariably suffers
from unplanned dataset biases, particularly multimodal utterance-level label
bias and word-level context bias. These harmful biases potentially mislead
models to focus on statistical shortcuts and spurious correlations, causing
severe performance bottlenecks. To alleviate these issues, we present a
Multimodal Counterfactual Inference Sentiment (MCIS) analysis framework based
on causality rather than conventional likelihood. Concretely, we first
formulate a causal graph to discover harmful biases from already-trained
vanilla models. In the inference phase, given a factual multimodal input, MCIS
imagines two counterfactual scenarios to purify and mitigate these biases.
Then, MCIS can make unbiased decisions from biased observations by comparing
factual and counterfactual outcomes. We conduct extensive experiments on
several standard MSA benchmarks. Qualitative and quantitative results show the
effectiveness of the proposed framework.
