Modeling context and knowledge for dialogue generation - Zheng, Wen (2023)
Dialogue generation reflects the cutting-edge AI technology application, with several AI assistants already developed by prominent Information and Communications Technology (ICT) companies like Google Assistant, Apple Siri, and Microsoft Cortana. However, AI support for open dialogue presents many challenges and research continues to address them, including the ultimate challenge of passing the Turing test. One of the key issues in dialogue generation is the source of content that can be used to train generative models within dialogue systems. Over the past decade, the construction of dialogue-generation datasets (e.g., Wizard of Wikipedia, CMU-DoG, DailyDialog, etc.) and research on models for context-aware and knowledge-based dialogue generation have been actively pursued by researchers. A detailed review of the related literature has revealed that the field has rapidly evolved, leading to significant progress but also giving rise to new questions and challenges. In this thesis, this line of research is continued, and specific issues in context-aware and knowledge-based dialogue generation are addressed, including (1) Context Usage in Dialogue Generation, incorporating dialogue intrinsic properties related to the speakers and content characteristics; (2) Knowledge Injection in Dialogue Generation, enabling the incorporation of multiple sources of knowledge; (3) Knowledge Selection in Dialogue Generation, with flexibility to separate the injection of knowledge from dialogue generation; (4) Term-Level Knowledge De-noising in Dialogue Generation, simulating response representations that can be used for knowledge de-noising in the test phase; and (5) Differentiating Context Use for Knowledge Selection and Response Generation, supporting a distinct use of context and contextualized knowledge for selecting knowledge and generating responses.