Summary of Identification Of Entailment and Contradiction Relations Between Natural Language Sentences: a Neurosymbolic Approach, by Xuyao Feng and Anthony Hunter
Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach
by Xuyao Feng, Anthony Hunter
First submitted to arxiv on: 2 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper proposes an explainable approach to natural language inference (NLI) and Recognizing Textual Entailment (RTE), which is crucial for understanding human language. The authors use a novel pipeline that translates text into Abstract Meaning Representation (AMR) graphs, then converts them into propositional logic using a SAT solver. To address the issue of different wordings not being identified as logical representations, the paper introduces relaxation methods to allow replacement or forgetting of some propositions. The proposed pipeline is evaluated on four RTE datasets and shows promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand human language better by explaining how we can tell if a statement follows logically from another one. Right now, most research in this area uses machine learning and deep learning, but these methods aren’t easy to understand or explain. To change this, the authors created a new way of doing NLI that is based on translating text into a special kind of graph. They then use a computer program to reason about this graph and figure out if one statement follows from another. The paper shows that this approach works well on four different datasets. |
Keywords
» Artificial intelligence » Deep learning » Inference » Machine learning
