This paper has been retracted. An error occurred in the preprocessing of the pitch and intensity features that this model used. This error means that it can no longer be concluded that prosody is as helpful for finding sentence boundaries and parsing as asserted in this paper.
Abstract
Parsing spoken dialogue poses unique difficulties, including disfluencies and unmarked boundaries between sentence-like units. Previous work has shown that prosody can help with parsing disfluent speech (Tran et al. 2018), but has assumed that the input to the parser is already segmented into sentence-like units (SUs), which isn’t true in existing speech applications. We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model). In experiments on the English Switchboard corpus, we find that when using transcripts alone, the turn-based model has trouble segmenting SUs, leading to worse parse performance than the SU-based model. However, prosody can effectively replace gold standard SU boundaries: with prosody, the turn-based model performs as well as the SU-based model (91.38 vs. 91.06 F1 score, respectively), despite performing two tasks (SU segmentation and parsing) rather than one (parsing alone). Analysis shows that pitch and intensity features are the most important for this corpus, since they allow the model to correctly distinguish an SU boundary from a speech disfluency – a distinction that the model otherwise struggles to make.- Anthology ID:
- 2021.acl-long.79
- Original:
- 2021.acl-long.79v1
- Version 2:
- 2021.acl-long.79v2
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 979–992
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.79
- DOI:
- 10.18653/v1/2021.acl-long.79
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.acl-long.79.pdf