Abstract
In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals with it by applying classifiers with rich features. Most of the current neural network models still omit the sequential information in the conversation. Henceforth, we apply a novel multi-level gated recurrent neural network (GRNN) with non-textual information to predict the DA tag. Our model not only utilizes textual information, but also makes use of non-textual and contextual information. In comparison, our model has shown significant improvement over previous works on Switchboard Dialog Act (SWDA) task by over 6%.- Anthology ID:
- C16-1185
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1970–1979
- Language:
- URL:
- https://aclanthology.org/C16-1185
- DOI:
- Cite (ACL):
- Wei Li and Yunfang Wu. 2016. Multi-level Gated Recurrent Neural Network for dialog act classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1970–1979, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Multi-level Gated Recurrent Neural Network for dialog act classification (Li & Wu, COLING 2016)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/C16-1185.pdf