DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog
Chia-Chien Hung, Anne Lauscher, Simone Ponzetto, Goran Glavaš
Abstract
Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, exploit general dialogic corpora (e.g., Reddit) and thus presumably fail to reliably embed domain-specific knowledge useful for concrete downstream TOD domains. In this work, we investigate the effects of domain specialization of pretrained language models (PLMs) for TOD. Within our DS-TOD framework, we first automatically extract salient domain-specific terms, and then use them to construct DomainCC and DomainReddit – resources that we leverage for domain-specific pretraining, based on (i) masked language modeling (MLM) and (ii) response selection (RS) objectives, respectively. We further propose a resource-efficient and modular domain specialization by means of domain adapters – additional parameter-light layers in which we encode the domain knowledge. Our experiments with prominent TOD tasks – dialog state tracking (DST) and response retrieval (RR) – encompassing five domains from the MultiWOZ benchmark demonstrate the effectiveness of DS-TOD. Moreover, we show that the light-weight adapter-based specialization (1) performs comparably to full fine-tuning in single domain setups and (2) is particularly suitable for multi-domain specialization, where besides advantageous computational footprint, it can offer better TOD performance.- Anthology ID:
- 2022.findings-acl.72
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 891–904
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.72
- DOI:
- 10.18653/v1/2022.findings-acl.72
- Cite (ACL):
- Chia-Chien Hung, Anne Lauscher, Simone Ponzetto, and Goran Glavaš. 2022. DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog. In Findings of the Association for Computational Linguistics: ACL 2022, pages 891–904, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog (Hung et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.72.pdf
- Code
- umanlp/ds-tod
- Data
- CCNet