论文标题
注入以任务为导向对话系统的语言模型中的域知识
Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems
论文作者
论文摘要
预训练的语言模型（PLM）已在NLP应用程序中提出了最新的范围，但是缺乏特定于领域的知识，而这些知识并非自然发生在训练数据中。先前的研究增强了对不同下游NLP任务的象征性知识的PLM。但是，这些研究中使用的知识库（KB）通常是大规模且静态的，与在现实面向任务的对话（TOD）系统中突出的小型，域特异性和可修改的知识库相反。在本文中，我们展示了在对TOD任务进行微调之前注入特定于域知识的优势。为此，我们利用了可以轻松与PLM集成的轻量级适配器，并用作从不同KB中学到的事实的存储库。为了衡量提出的知识注入方法的疗效，我们使用响应选择（KPRS）引入知识探测，这是一种专门为TOD模型设计的探针。 KPRS和响应生成任务的实验表明，适配器对强基础的知识注入改进。
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.
