Papers by Xiaohuan Cao

2 papers
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors (2022.findings-acl)

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Challenge: Existing models for multimodal sentiment analysis are limited in their capacity to be deployed in the real world.
Approach: They propose a model that can dynamically refine erroneous sentiment words by leveraging multimodal sentiment clues.
Outcome: The proposed model surpasses the state-of-the-art models on three datasets.
Enhancing Multiple-choice Machine Reading Comprehension by Punishing Illogical Interpretations (2021.emnlp-main)

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Challenge: Multiple-choice MRC is one of the most studied tasks in MRC due to the convenience of evaluation and the flexibility of answer format.
Approach: They propose to use multiple-choice MRC to explain a trained model and reveal how it arrives at the prediction by punishing illogical attributions.
Outcome: The proposed method improves model performance without external information and model structure change without any external information.

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