Abstract
We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a novel attentional technique and a label to label connection for sequence learning, akin to Hidden Markov Models. The experiments show that both of these innovations lead our model to outperform strong baselines for dialogue act classification on MapTask and Switchboard corpora. We further empirically analyse the effectiveness of each of the new innovations.- Anthology ID:
- P17-2083
- 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:
- 524–529
- Language:
- URL:
- https://aclanthology.org/P17-2083
- DOI:
- 10.18653/v1/P17-2083
- Cite (ACL):
- Quan Hung Tran, Gholamreza Haffari, and Ingrid Zukerman. 2017. A Generative Attentional Neural Network Model for Dialogue Act Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 524–529, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- A Generative Attentional Neural Network Model for Dialogue Act Classification (Tran et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/P17-2083.pdf