Abstract
This paper is about learning word representations using grammatical type information. We use the syntactic types of Combinatory Categorial Grammar to develop multilinear representations, i.e. maps with n arguments, for words with different functional types. The multilinear maps of words compose with each other to form sentence representations. We extend the skipgram algorithm from vectors to multi- linear maps to learn these representations and instantiate it on unary and binary maps for transitive verbs. These are evaluated on verb and sentence similarity and disambiguation tasks and a subset of the SICK relatedness dataset. Our model performs better than previous type- driven models and is competitive with state of the art representation learning methods such as BERT and neural sentence encoders.- Anthology ID:
- 2020.conll-1.24
- Volume:
- Proceedings of the 24th Conference on Computational Natural Language Learning
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Raquel Fernández, Tal Linzen
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 313–324
- Language:
- URL:
- https://aclanthology.org/2020.conll-1.24
- DOI:
- 10.18653/v1/2020.conll-1.24
- Cite (ACL):
- Gijs Wijnholds, Mehrnoosh Sadrzadeh, and Stephen Clark. 2020. Representation Learning for Type-Driven Composition. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 313–324, Online. Association for Computational Linguistics.
- Cite (Informal):
- Representation Learning for Type-Driven Composition (Wijnholds et al., CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.conll-1.24.pdf
- Code
- gijswijnholds/tensorskipgram-torch
- Data
- SICK