Abstract
One main challenge in building task-oriented dialogue systems is the limited amount of supervised training data available. In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset. We show that pretraining using unlabeled data can bring better model performance with a 31% boost in Recall@1 compared with no pretraining. The proposed finetuning technique based on a small amount of high-quality, annotated data resulted in 26% offline and 33% online performance improvement in Recall@1 over the pretrained model. The model is deployed in an agent-support application and evaluated on live customer service contacts, providing additional insights into the real-world implications compared with most other publications in the domain often using asynchronous transcripts (e.g. Reddit data). The high performance of 74% Recall@1 shown in the customer service example demonstrates the effectiveness of this pretrain-finetune approach in dealing with the limited supervised data challenge.- Anthology ID:
- 2021.naacl-industry.5
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Young-bum Kim, Yunyao Li, Owen Rambow
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–40
- Language:
- URL:
- https://aclanthology.org/2021.naacl-industry.5
- DOI:
- 10.18653/v1/2021.naacl-industry.5
- Cite (ACL):
- Manisha Srivastava, Yichao Lu, Riley Peschon, and Chenyang Li. 2021. Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers, pages 34–40, Online. Association for Computational Linguistics.
- Cite (Informal):
- Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting (Srivastava et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.naacl-industry.5.pdf