Abstract
Fixed-vocabulary language models fail to account for one of the most characteristic statistical facts of natural language: the frequent creation and reuse of new word types. Although character-level language models offer a partial solution in that they can create word types not attested in the training corpus, they do not capture the “bursty” distribution of such words. In this paper, we augment a hierarchical LSTM language model that generates sequences of word tokens character by character with a caching mechanism that learns to reuse previously generated words. To validate our model we construct a new open-vocabulary language modeling corpus (the Multilingual Wikipedia Corpus; MWC) from comparable Wikipedia articles in 7 typologically diverse languages and demonstrate the effectiveness of our model across this range of languages.- Anthology ID:
- P17-1137
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1492–1502
- Language:
- URL:
- https://aclanthology.org/P17-1137
- DOI:
- 10.18653/v1/P17-1137
- Cite (ACL):
- Kazuya Kawakami, Chris Dyer, and Phil Blunsom. 2017. Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1492–1502, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Learning to Create and Reuse Words in Open-Vocabulary Neural Language Modeling (Kawakami et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1137.pdf
- Data
- WikiText-2