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

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