Summary of Integrating Hierarchical Semantic Into Iterative Generation Model For Entailment Tree Explanation, by Qin Wang et al.
Integrating Hierarchical Semantic into Iterative Generation Model for Entailment Tree Explanation
by Qin Wang, Jianzhou Feng, Yiming Xu
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel architecture, HiSCG, for explainable question answering (QA) that integrates hierarchical semantics of sentences. It addresses the issue of apparent mistakes in combinations by designing a hierarchical mapping between hypotheses and facts, discriminating facts involved in tree constructions, and optimizing single-step entailments. The proposed method achieves comparable performance on the EntailmentBank dataset and demonstrates effectiveness on two out-of-domain datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps answer questions more clearly by understanding how sentences relate to each other. It makes a new approach that looks at both the relationships between sentences within the same level and those between adjacent levels. This results in better performance on answering questions. |
Keywords
» Artificial intelligence » Question answering » Semantics
