Deploying a Retrieval based Response Model for Task Oriented Dialogues
Lahari Poddar, György Szarvas, Cheng Wang, Jorge Balazs, Pavel Danchenko, Patrick Ernst
Abstract
Task-oriented dialogue systems in industry settings need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints. This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates. First, we provide a simple algorithm to semi-automatically create a high-coverage template set from historic conversations without any annotation. Second, we propose a neural architecture that encodes the dialogue context and applicable business constraints as profile features for ranking the next turn. Third, we describe a two-stage learning strategy with self-supervised training, followed by supervised fine-tuning on limited data collected through a human-in-the-loop platform. Finally, we describe offline experiments and present results of deploying our model with human-in-the-loop to converse with live customers online.- Anthology ID:
- 2022.emnlp-industry.17
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, UAE
- Editors:
- Yunyao Li, Angeliki Lazaridou
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 169–178
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-industry.17
- DOI:
- 10.18653/v1/2022.emnlp-industry.17
- Cite (ACL):
- Lahari Poddar, György Szarvas, Cheng Wang, Jorge Balazs, Pavel Danchenko, and Patrick Ernst. 2022. Deploying a Retrieval based Response Model for Task Oriented Dialogues. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 169–178, Abu Dhabi, UAE. Association for Computational Linguistics.
- Cite (Informal):
- Deploying a Retrieval based Response Model for Task Oriented Dialogues (Poddar et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-industry.17.pdf