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

• URL: http://arxiv.org/abs/2212.08120v1
• Date: Thu, 15 Dec 2022 20:15:05 GMT
• Title: Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
• Authors: Denis Emelin, Daniele Bonadiman, Sawsan Alqahtani, Yi Zhang, Saab Mansour
• Abstract summary: Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications. They 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.
• Score: 9.983102639594899
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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