Neural Natural Logic Inference for Interpretable Question Answering
Jihao Shi, Xiao Ding, Li Min Du, Ting Liu, Bing Qin · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses.In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models.The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths.Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis.As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations.Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets.The natural logic inference process inherently provides evidence to help explain the prediction process.