Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909188079222784 |
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| author | Feng, Xuyao Hunter, Anthony |
| author_facet | Feng, Xuyao Hunter, Anthony |
| contents | Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this task on specific datasets, meaning their solution is not explainable nor explicit. To address the need for an explainable approach to RTE, we propose a novel pipeline that is based on translating text into an Abstract Meaning Representation (AMR) graph. For this we use a pre-trained AMR parser. We then translate the AMR graph into propositional logic and use a SAT solver for automated reasoning. In text, often commonsense suggests that an entailment (or contradiction) relationship holds between a premise and a claim, but because different wordings are used, this is not identified from their logical representations. To address this, we introduce relaxation methods to allow replacement or forgetting of some propositions. Our experimental results show this pipeline performs well on four RTE datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01259 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach Feng, Xuyao Hunter, Anthony Artificial Intelligence Computation and Language I.2 Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this task on specific datasets, meaning their solution is not explainable nor explicit. To address the need for an explainable approach to RTE, we propose a novel pipeline that is based on translating text into an Abstract Meaning Representation (AMR) graph. For this we use a pre-trained AMR parser. We then translate the AMR graph into propositional logic and use a SAT solver for automated reasoning. In text, often commonsense suggests that an entailment (or contradiction) relationship holds between a premise and a claim, but because different wordings are used, this is not identified from their logical representations. To address this, we introduce relaxation methods to allow replacement or forgetting of some propositions. Our experimental results show this pipeline performs well on four RTE datasets. |
| title | Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach |
| topic | Artificial Intelligence Computation and Language I.2 |
| url | https://arxiv.org/abs/2405.01259 |