Abstract
There is a growing awareness of the need to handle rare and unseen words in word representation modelling. In this paper, we focus on few-shot learning of emerging concepts that fully exploits only a few available contexts. We introduce a substitute-based context representation technique that can be applied on an existing word embedding space. Previous context-based approaches to modelling unseen words only consider bag-of-word first-order contexts, whereas our method aggregates contexts as second-order substitutes that are produced by a sequence-aware sentence completion model. We experimented with three tasks that aim to test the modelling of emerging concepts. We found that these tasks show different emphasis on first and second order contexts, and our substitute-based method achieves superior performance on naturally-occurring contexts from corpora.- Anthology ID:
- S19-1007
- Volume:
- Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- *SEM
- SIGs:
- SIGSEM | SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 61–67
- Language:
- URL:
- https://aclanthology.org/S19-1007
- DOI:
- 10.18653/v1/S19-1007
- Cite (ACL):
- Qianchu Liu, Diana McCarthy, and Anna Korhonen. 2019. Second-order contexts from lexical substitutes for few-shot learning of word representations. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 61–67, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Second-order contexts from lexical substitutes for few-shot learning of word representations (Liu et al., *SEM 2019)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/S19-1007.pdf