Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey
Longxuan Ma, Mingda Li, Weinan Zhang, Jiapeng Li, Ting Liu

TL;DR
This survey comprehensively reviews unstructured text enhanced open-domain dialogue systems, analyzing their models, datasets, evaluation methods, and future trends to advance research in knowledge-integrated dialogue generation.
Contribution
It systematically categorizes UTEDS models, summarizes recent datasets and evaluation techniques, and discusses future directions, providing a structured overview of this emerging field.
Findings
Retrieval models include Fusion, Matching, and Ranking modules.
Generative models involve Dialogue and Knowledge Encoding, Knowledge Selection, Response Generation.
Current models show promising performance but face challenges in knowledge integration.
Abstract
Incorporating external knowledge into dialogue generation has been proven to benefit the performance of an open-domain Dialogue System (DS), such as generating informative or stylized responses, controlling conversation topics. In this article, we study the open-domain DS that uses unstructured text as external knowledge sources (\textbf{U}nstructured \textbf{T}ext \textbf{E}nhanced \textbf{D}ialogue \textbf{S}ystem, \textbf{UTEDS}). The existence of unstructured text entails distinctions between UTEDS and traditional data-driven DS and we aim to analyze these differences. We first give the definition of the UTEDS related concepts, then summarize the recently released datasets and models. We categorize UTEDS into Retrieval and Generative models and introduce them from the perspective of model components. The retrieval models consist of Fusion, Matching, and Ranking modules, while the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
