Heejae Yu  Sang-goo Lee

10.5626/JOK.2024.51.11.1028

conversational search query reformulation Coreference Resolution generative languagemodel data augmentation

Conversational search enables retrieval of relevant passages for a current turn query by understanding the contextual meaning in a multi-turn dialogue. In conversational search, Conversational Query Reformulation enables utilization of off-the-shelf retrievers by transforming context-dependent queries into self-contained forms. Existing approaches primarily fine-tune pre-trained language models using human-rewritten queries as labels or prompt large language models (LLMs) to address ambiguity inherent in the current turn query, such as ellipsis and coreference. However, our preliminary experimental results indicate that existing models continue to face challenges with coreference resolution. This paper addresses two main research questions: 1) Can a model be trained to distinguish anaphoric mentions that need further clarification? And 2) Can a model be trained to clarify detected coreference mentions into more specified phrases? To investigate these questions, we devised two main components - the detector and the decoder. Our experiments demonstrated that our fine-tuned detector could identify diverse anaphoric phrases within questions, while our fine-tuned decoder could successfully clarify them, ultimately enabling effective coreference resolution for query rewriting. Therefore, we present a novel paradigm, Coreference Aware Conversational Query Reformulation, utilizing these main components.