Abstract
Slot filling is a core operation for utterance understanding in task-oriented dialogue systems. Slots are typically domain-specific, and adding new domains to a dialogue system involves data and time-intensive processes. A popular technique to address the problem is transfer learning, where it is assumed the availability of a large slot filling dataset for the source domain, to be used to help slot filling on the target domain, with fewer data. In this work, instead, we propose to leverage source tasks based on semantically related non-conversational resources (e.g., semantic sequence tagging datasets), as they are both cheaper to obtain and reusable to several slot filling domains. We show that using auxiliary non-conversational tasks in a multi-task learning setup consistently improves low resource slot filling performance.- Anthology ID:
- W19-5911
- Volume:
- Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue
- Month:
- September
- Year:
- 2019
- Address:
- Stockholm, Sweden
- Editors:
- Satoshi Nakamura, Milica Gasic, Ingrid Zuckerman, Gabriel Skantze, Mikio Nakano, Alexandros Papangelis, Stefan Ultes, Koichiro Yoshino
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 85–91
- Language:
- URL:
- https://aclanthology.org/W19-5911
- DOI:
- 10.18653/v1/W19-5911
- Cite (ACL):
- Samuel Louvan and Bernardo Magnini. 2019. Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue, pages 85–91, Stockholm, Sweden. Association for Computational Linguistics.
- Cite (Informal):
- Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help? (Louvan & Magnini, SIGDIAL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-5911.pdf