Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems

Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour


Abstract
Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data. Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks. However, knowledge bases (KBs) utilized in these studies are usually large-scale and static, in contrast to small, domain-specific, and modifiable knowledge bases that are prominent in real-world task-oriented dialogue (TOD) systems. In this paper, we showcase the advantages of injecting domain-specific knowledge prior to fine-tuning on TOD tasks. To this end, we utilize light-weight adapters that can be easily integrated with PLMs and serve as a repository for facts learned from different KBs. To measure the efficacy of proposed knowledge injection methods, we introduce Knowledge Probing using Response Selection (KPRS) – a probe designed specifically for TOD models. Experiments on KPRS and the response generation task show improvements of knowledge injection with adapters over strong baselines.
Anthology ID:
2022.emnlp-main.820
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11962–11974
Language:
URL:
https://aclanthology.org/2022.emnlp-main.820
DOI:
Bibkey:
Cite (ACL):
Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, and Saab Mansour. 2022. Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11962–11974, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Injecting Domain Knowledge in Language Models for Task-oriented Dialogue Systems (Emelin et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.820.pdf