Utterance Position-Aware Dialogue Act Recognition
Yuki Yano, Akihiro Tamura, Takashi Ninomiya, Hiroaki Obayashi
Abstract
This study proposes an utterance position-aware approach for a neural network-based dialogue act recognition (DAR) model, which incorporates positional encoding for utterance’s absolute or relative position. The proposed approach is inspired by the observation that some dialogue acts have tendencies of occurrence positions. The evaluations on the Switchboard corpus show that the proposed positional encoding of utterances statistically significantly improves the performance of DAR.- Anthology ID:
- 2021.ranlp-1.176
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 1567–1574
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2021.ranlp-1.176/
- DOI:
- Cite (ACL):
- Yuki Yano, Akihiro Tamura, Takashi Ninomiya, and Hiroaki Obayashi. 2021. Utterance Position-Aware Dialogue Act Recognition. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1567–1574, Held Online. INCOMA Ltd..
- Cite (Informal):
- Utterance Position-Aware Dialogue Act Recognition (Yano et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2021.ranlp-1.176.pdf