Abstract
Backchannels, which refer to short and often affirmative or empathetic responses from a listener during a conversation, play a crucial role in effective communication. In this paper, we introduce CABP(Context-Aware Backchannel Prediction), a sequential and attentive context approach aimed at enhancing backchannel prediction performance. Additionally, CABP leverages the pretrained wav2vec model for encoding audio signal. Experimental results show that CABP performs better than context-free models, with performance improvements of 1.3% and 1.8% in Korean and English datasets, respectively. Furthermore, when utilizing the pretrained wav2vec model, CABP consistently demonstrates the best performance, achieving performance improvements of 4.4% and 3.1% in Korean and English datasets.- Anthology ID:
- 2024.findings-eacl.118
- Volume:
- Findings of the Association for Computational Linguistics: EACL 2024
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1689–1694
- Language:
- URL:
- https://aclanthology.org/2024.findings-eacl.118
- DOI:
- Cite (ACL):
- Yo-Han Park, Wencke Liermann, Yong-Seok Choi, and Kong Joo Lee. 2024. Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness. In Findings of the Association for Computational Linguistics: EACL 2024, pages 1689–1694, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness (Park et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.findings-eacl.118.pdf