Knowledge-grounded dialogue modelling with dialogue-state tracking, domain tracking, and entity extraction

Abstract

As knowledge-grounded dialogue systems are attracting intense research interest, technology that facilitates reference to various types of external knowledge as dialogue-system data is also developing. A knowledge-grounded dialogue system must be capable of (1) accurately interpret the conversation content, (2) determining whether external knowledge should be referenced for the current turn, (3) identifying the external knowledge to be referenced, and (4) generating a response. If the referenced external knowledge is in the form of a question-answer pair, this pair is closely related to the domain to which the external knowledge belongs and the entity to which it pertains. This study proposes a knowledge-grounded dialogue system that predicts the domain and entity associated with a question-answer pair referenced by the dialogue system; the system then leverages this information to effectively implement the above three external-knowledge-based capabilities. As the dialogue data associated with external knowledge is diverse, adaptation to the slots and entities of different dialogue datasets is challenging. In this study, the Triple Copy (TripPy) model, which is one of the leading benchmark models for dialogue-state tracking with the Multi-domain Wizard-of-Oz (MultiWOZ) dataset, is further developed to adapt to DSTC10 data for the external knowledge-based dialogue system; hence, dialogue content is effectively interpreted. The developed knowledge-grounded dialogue system incorporates knowledge-seeking turn detection, knowledge selection, and knowledge-grounded response generation models. The model, in DSTC10 dataset, achieves 15.49%p, 12.54%p, and 36.45%p improvements over the baseline, the state-of-the-art model for the previous version of the data (DSTC9 dataset), in terms of the F1 score, Recall@1 score, and BLEU-1 score, respectively. Moreover, in a dialogue-state tracking task for DSTC10 dataset, a 15.41%p improvement in joint goal accuracy score is achieved compared to the TripPy model.