通过层次语义增强推理树生成，提升问答可解释性。
Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation
- 构建控制器-生成器框架，显式建模句间层次语义关系。
- 在三个设定下达到可比性能，跨域数据集表现稳健。
- 适合关注逻辑推理与可解释AI的研究者。
清晰且合乎逻辑地展示从证据到答案的推理路径，对可解释问答（QA）至关重要。蕴含树结构化呈现了推理链条，与大规模语言模型的自解释原则不同。现有方法很少考虑树结构中层内及层间句子的语义关联，易导致组合错误。本文提出一种在控制器-生成器框架下的层次语义整合架构（HiSCG），设计假设与事实间的分层映射，识别构建树时涉及的事实，并优化单步蕴含。据我们所知，这是首个关注同层及相邻层间句子层次语义以实现改进的工作。该方法在EntailmentBank数据集的三种设置下均取得可比性能，且在两个跨域数据集上展现出良好泛化能力。
原文摘要 · Abstract (English)
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.
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