计算机科学
情态动词
推论
背景(考古学)
强化学习
特征(语言学)
判决
人工智能
过程(计算)
特征提取
机器学习
自然语言处理
程序设计语言
古生物学
语言学
化学
哲学
高分子化学
生物
作者
Fanrui Zhang,J. Liu,Qiang Zhang,Edward Sun,Jingyi Xie,Zheng-Jun Zha
标识
DOI:10.1145/3581783.3612183
摘要
Recently, falsified claims incorporating both text and images have been disseminated more effectively than those containing text alone, raising significant concerns for multi-modal fact verification. Existing research makes contributions to multi-modal feature extraction and interaction, but fails to fully utilize and enhance the valuable and intricate semantic relationships between distinct features. Moreover, most detectors merely provide a single outcome judgment and lack an inference process or explanation. Taking these factors into account, we propose a novel Explainable and Context-Enhanced Network (ECENet) for multi-modal fact verification, making the first attempt to integrate multi-clue feature extraction, multi-level feature reasoning, and justification (explanation) generation within a unified framework. Specifically, we propose an Improved Coarse- and Fine-grained Attention Network, equipped with two types of level-grained attention mechanisms, to facilitate a comprehensive understanding of contextual information. Furthermore, we propose a novel justification generation module via deep reinforcement learning that does not require additional labels. In this module, a sentence extractor agent measures the importance between the query claim and all document sentences at each time step, selecting a suitable amount of high-scoring sentences to be rewritten as the explanation of the model. Extensive experiments demonstrate the effectiveness of the proposed method.
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