Abstract
In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11%, and MRDA by 2.2%.- Anthology ID:
- C16-1189
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2012–2021
- Language:
- URL:
- https://aclanthology.org/C16-1189
- DOI:
- Cite (ACL):
- Hamed Khanpour, Nishitha Guntakandla, and Rodney Nielsen. 2016. Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2012–2021, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network (Khanpour et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1189.pdf