Abstract
We test whether distributional models can do one-shot learning of definitional properties from text only. Using Bayesian models, we find that first learning overarching structure in the known data, regularities in textual contexts and in properties, helps one-shot learning, and that individual context items can be highly informative.- Anthology ID:
- I17-1021
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 204–213
- Language:
- URL:
- https://aclanthology.org/I17-1021
- DOI:
- Cite (ACL):
- Su Wang, Stephen Roller, and Katrin Erk. 2017. Distributional Modeling on a Diet: One-shot Word Learning from Text Only. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 204–213, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Distributional Modeling on a Diet: One-shot Word Learning from Text Only (Wang et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/I17-1021.pdf