Abstract
Lexical entailment (LE; also known as hyponymy-hypernymy or is-a relation) is a core asymmetric lexical relation that supports tasks like taxonomy induction and text generation. In this work, we propose a simple and effective method for fine-tuning distributional word vectors for LE. Our Generalized Lexical ENtailment model (GLEN) is decoupled from the word embedding model and applicable to any distributional vector space. Yet – unlike existing retrofitting models – it captures a general specialization function allowing for LE-tuning of the entire distributional space and not only the vectors of words seen in lexical constraints. Coupled with a multilingual embedding space, GLEN seamlessly enables cross-lingual LE detection. We demonstrate the effectiveness of GLEN in graded LE and report large improvements (over 20% in accuracy) over state-of-the-art in cross-lingual LE detection.- Anthology ID:
- P19-1476
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4824–4830
- Language:
- URL:
- https://aclanthology.org/P19-1476
- DOI:
- 10.18653/v1/P19-1476
- Cite (ACL):
- Goran Glavaš and Ivan Vulić. 2019. Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4824–4830, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Generalized Tuning of Distributional Word Vectors for Monolingual and Cross-Lingual Lexical Entailment (Glavaš & Vulić, ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P19-1476.pdf
- Code
- codogogo/glen
- Data
- HyperLex