Table-based Fact Verification with Self-adaptive Mixture of Experts

Yuxuan Zhou; Xien Liu; Kaiyin Zhou; Ji Wu

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arXiv · 2204.08753

Table-based Fact Verification with Self-adaptive Mixture of Experts

Yuxuan Zhou·Xien Liu·Kaiyin Zhou·Ji Wu

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Abstract

The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem. It inherently requires informative reasoning over natural language together with different numerical and logical reasoning on tables (e.g., count, superlative, comparative). Considering that, we exploit mixture-of-experts and present in this paper a new method: Self-adaptive Mixture-of-Experts Network (SaMoE). Specifically, we have developed a mixture-of-experts neural network to recognize and execute different types of reasoning -- the network is composed of multiple experts, each handling a specific part of the semantics for reasoning, whereas a management module is applied to decide the contribution of each expert network to the verification result. A self-adaptive method is developed to teach the management module combining results of different experts more efficiently without external knowledge. The experimental results illustrate that our framework achieves 85.1% accuracy on the benchmark dataset TabFact, comparable with the previous state-of-the-art models. We hope our framework can serve as a new baseline for table-based verification. Our code is available at https://github.com/THUMLP/SaMoE.

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BibTeX RIS

Yuxuan Zhou, Xien Liu, Kaiyin Zhou, Ji Wu. 2022-04-19. Table-based Fact Verification with Self-adaptive Mixture of Experts. https://arxiv.org/abs/2204.08753

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