Abstract
While being an essential component of spoken language, fillers (e.g. “um” or “uh”) often remain overlooked in Spoken Language Understanding (SLU) tasks. We explore the possibility of representing them with deep contextualised embeddings, showing improvements on modelling spoken language and two downstream tasks — predicting a speaker’s stance and expressed confidence.- Anthology ID:
- 2020.emnlp-main.641
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7985–7993
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.641
- DOI:
- 10.18653/v1/2020.emnlp-main.641
- Cite (ACL):
- Tanvi Dinkar, Pierre Colombo, Matthieu Labeau, and Chloé Clavel. 2020. The importance of fillers for text representations of speech transcripts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7985–7993, Online. Association for Computational Linguistics.
- Cite (Informal):
- The importance of fillers for text representations of speech transcripts (Dinkar et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.emnlp-main.641.pdf