Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation
This addresses the need for explainable QA by improving logical reasoning displays, though it appears incremental as it builds on existing entailment tree methods.
The paper tackles the problem of generating entailment tree explanations for question answering by integrating hierarchical semantic associations between sentences, achieving comparable performance on the EntailmentBank dataset and demonstrating generalization on out-of-domain datasets.
Manifestly and logically displaying the line of reasoning from evidence to answer is significant to explainable question answering (QA). The entailment tree exhibits the lines structurally, which is different from the self-explanation principle in large-scale language models. Existing methods rarely consider the semantic association of sentences between and within hierarchies within the tree structure, which is prone to apparent mistakes in combinations. In this work, we propose an architecture of integrating the Hierarchical Semantics of sentences under the framework of Controller-Generator (HiSCG) to explain answers. The HiSCG designs a hierarchical mapping between hypotheses and facts, discriminates the facts involved in tree constructions, and optimizes single-step entailments. To the best of our knowledge, We are the first to notice hierarchical semantics of sentences between the same layer and adjacent layers to yield improvements. The proposed method achieves comparable performance on all three settings of the EntailmentBank dataset. The generalization results on two out-of-domain datasets also demonstrate the effectiveness of our method.