Hierarchical Pre-training for Sequence Labelling in Spoken Dialog
Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, Chloé Clavel
Abstract
Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.- Anthology ID:
- 2020.findings-emnlp.239
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2636–2648
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.239
- DOI:
- 10.18653/v1/2020.findings-emnlp.239
- Cite (ACL):
- Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, and Chloé Clavel. 2020. Hierarchical Pre-training for Sequence Labelling in Spoken Dialog. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2636–2648, Online. Association for Computational Linguistics.
- Cite (Informal):
- Hierarchical Pre-training for Sequence Labelling in Spoken Dialog (Chapuis et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.findings-emnlp.239.pdf
- Data
- SILICONE Benchmark, DailyDialog, EmotionLines, IEMOCAP, MELD, MRDA, OpenSubtitles, SEMAINE, Switchboard-1 Corpus