A Model-Theoretic Formalization of Natural Language Inference Using Neural Network and Tableau Method
A Model-Theoretic Formalization of Natural Language Inference Using Neural Network and Tableau Method
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发表时间：
2022
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通讯作者：
Ayahito Saji;Yoshihide Kato;S. Matsubara
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作者：
Ayahito Saji;Yoshihide Kato;S. Matsubara
Saji等人（2021）将基于神经的NLI模型和符号模型集成到一个框架中，以获得两个世界的最佳效果。这个框架是基于一个tableau方法，这是一个证明系统的形式逻辑;然而，它有一个遗留的问题，它的理论局限性还没有明确的艾德。为了解决这个问题，本文从理论上对框架模型进行了形式化。在形式化的基础上，我们证明了这种框架具有一定的合理性，而不具有完整性。
Saji et al. (2021) integrated a neural-based NLI model and a symbolic one into a framework to get the best of both worlds. This framework is based on a tableau method, which is a proof system for formal logic; however, it has a remaining issue that its theoretical limitations have not been clarified. To solve this issue, this paper formalizes the framework model-theoretically. On the basis of the formalization, we demonstrate that a certain kind of soundness holds for this framework, while the completeness does not.