Table-based Fact Verification with Self-adaptive Mixture of Experts
Yuxuan Zhou, Xien Liu, Kaiyin Zhou, Ji Wu · Findings of the Association for Computational Linguistics: ACL 2022 · 2022
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 Mixtureof-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 TAB-FACT, comparable with the previous state-ofthe-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.