How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Zhiliang Tian, Rui Yan, Lili Mou, Yiping Song, Yansong Feng, Dongyan Zhao
Abstract
Generative conversational systems are attracting increasing attention in natural language processing (NLP). Recently, researchers have noticed the importance of context information in dialog processing, and built various models to utilize context. However, there is no systematic comparison to analyze how to use context effectively. In this paper, we conduct an empirical study to compare various models and investigate the effect of context information in dialog systems. We also propose a variant that explicitly weights context vectors by context-query relevance, outperforming the other baselines.- Anthology ID:
- P17-2036
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 231–236
- Language:
- URL:
- https://aclanthology.org/P17-2036
- DOI:
- 10.18653/v1/P17-2036
- Cite (ACL):
- Zhiliang Tian, Rui Yan, Lili Mou, Yiping Song, Yansong Feng, and Dongyan Zhao. 2017. How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 231–236, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models (Tian et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-bitext-workshop/P17-2036.pdf