  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.

Artificial Intelligence;Computation and Language;Machine Learning