S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

• URL: http://arxiv.org/abs/2309.02155v1
• Date: Tue, 5 Sep 2023 11:47:51 GMT
• Title: S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning
• Authors: Wei Suo, Mengyang Sun, Weisong Liu, Yiqi Gao, Peng Wang, Yanning Zhang, Qi Wu
• Abstract summary: VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. We propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C) S3C evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales.
• Score: 46.787034512390434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the S3C can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.

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