Abstract
This paper presents a model for disfluency detection in spontaneous speech transcripts called LSTM Noisy Channel Model. The model uses a Noisy Channel Model (NCM) to generate n-best candidate disfluency analyses and a Long Short-Term Memory (LSTM) language model to score the underlying fluent sentences of each analysis. The LSTM language model scores, along with other features, are used in a MaxEnt reranker to identify the most plausible analysis. We show that using an LSTM language model in the reranking process of noisy channel disfluency model improves the state-of-the-art in disfluency detection.- Anthology ID:
- P17-2087
- 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:
- 547–553
- Language:
- URL:
- https://aclanthology.org/P17-2087
- DOI:
- 10.18653/v1/P17-2087
- Cite (ACL):
- Paria Jamshid Lou and Mark Johnson. 2017. Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 547–553, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model (Jamshid Lou & Johnson, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/P17-2087.pdf