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        <title>Table-based Fact Verification with Self-adaptive Mixture of Experts</title>
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        <namePart type="given">Yuxuan</namePart>
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        <namePart type="given">Xien</namePart>
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    <name type="personal">
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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.</abstract>
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%0 Conference Proceedings
%T Table-based Fact Verification with Self-adaptive Mixture of Experts
%A Zhou, Yuxuan
%A Liu, Xien
%A Zhou, Kaiyin
%A Wu, Ji
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Findings of the Association for Computational Linguistics: ACL 2022
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F zhou-etal-2022-table
%X 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.
%R 10.18653/v1/2022.findings-acl.13
%U https://aclanthology.org/2022.findings-acl.13
%U https://doi.org/10.18653/v1/2022.findings-acl.13
%P 139-149

Markdown (Informal)

[Table-based Fact Verification with Self-adaptive Mixture of Experts](https://aclanthology.org/2022.findings-acl.13) (Zhou et al., Findings 2022)

• Table-based Fact Verification with Self-adaptive Mixture of Experts (Zhou et al., Findings 2022)

ACL

• Yuxuan Zhou, Xien Liu, Kaiyin Zhou, and Ji Wu. 2022. Table-based Fact Verification with Self-adaptive Mixture of Experts. In Findings of the Association for Computational Linguistics: ACL 2022, pages 139–149, Dublin, Ireland. Association for Computational Linguistics.