Abstract
The paradigm of leveraging large pre-trained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-to-end TOD modeling by adopting span prediction as an auxiliary task. In end-to-end setting, our model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.- Anthology ID:
- 2021.findings-emnlp.112
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1296–1303
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.112
- DOI:
- 10.18653/v1/2021.findings-emnlp.112
- Cite (ACL):
- Yohan Lee. 2021. Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1296–1303, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (Lee, Findings 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.findings-emnlp.112.pdf
- Code
- bepoetree/mttod
- Data
- MultiWOZ