Abstract
In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments. We propose to map phrases to their visual denotations and compare their meaning in terms of their images. We show that our approach is effective in the task of Recognizing Textual Entailment when combined with specific linguistic and logic features.- Anthology ID:
- D17-1305
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2853–2859
- Language:
- URL:
- https://aclanthology.org/D17-1305
- DOI:
- 10.18653/v1/D17-1305
- Cite (ACL):
- Dan Han, Pascual Martínez-Gómez, and Koji Mineshima. 2017. Visual Denotations for Recognizing Textual Entailment. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2853–2859, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Visual Denotations for Recognizing Textual Entailment (Han et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/D17-1305.pdf
- Data
- SICK, SNLI