Abstract
Tackling Natural Language Inference with a logic-based method is becoming less and less common. While this might have been counterintuitive several decades ago, nowadays it seems pretty obvious. The main reasons for such a conception are that (a) logic-based methods are usually brittle when it comes to processing wide-coverage texts, and (b) instead of automatically learning from data, they require much of manual effort for development. We make a step towards to overcome such shortcomings by modeling learning from data as abduction: reversing a theorem-proving procedure to abduce semantic relations that serve as the best explanation for the gold label of an inference problem. In other words, instead of proving sentence-level inference relations with the help of lexical relations, the lexical relations are proved taking into account the sentence-level inference relations. We implement the learning method in a tableau theorem prover for natural language and show that it improves the performance of the theorem prover on the SICK dataset by 1.4% while still maintaining high precision (>94%). The obtained results are competitive with the state of the art among logic-based systems.- Anthology ID:
- 2020.starsem-1.3
- Volume:
- Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- *SEM
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 20–31
- Language:
- URL:
- https://aclanthology.org/2020.starsem-1.3
- DOI:
- Cite (ACL):
- Lasha Abzianidze. 2020. Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference. In Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics, pages 20–31, Barcelona, Spain (Online). Association for Computational Linguistics.
- Cite (Informal):
- Learning as Abduction: Trainable Natural Logic Theorem Prover for Natural Language Inference (Abzianidze, *SEM 2020)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2020.starsem-1.3.pdf
- Code
- kovvalsky/LangPro
- Data
- SICK