Abstract
Language models are typically applied at the sentence level, without access to the broader document context. We present a neural language model that incorporates document context in the form of a topic model-like architecture, thus providing a succinct representation of the broader document context outside of the current sentence. Experiments over a range of datasets demonstrate that our model outperforms a pure sentence-based model in terms of language model perplexity, and leads to topics that are potentially more coherent than those produced by a standard LDA topic model. Our model also has the ability to generate related sentences for a topic, providing another way to interpret topics.- Anthology ID:
- P17-1033
- 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:
- 355–365
- Language:
- URL:
- https://aclanthology.org/P17-1033
- DOI:
- 10.18653/v1/P17-1033
- Cite (ACL):
- Jey Han Lau, Timothy Baldwin, and Trevor Cohn. 2017. Topically Driven Neural Language Model. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 355–365, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Topically Driven Neural Language Model (Lau et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P17-1033.pdf
- Code
- jhlau/topically-driven-language-model
- Data
- IMDb Movie Reviews