Generating Clarifying Questions for Query Refinement in Source Code Search
In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches. This strategy requires programmers to spend time reading search results that are irrelevant to their development needs. In contrast, when programmers seek information from other humans, they typically refine queries by asking and answering clarifying questions. Clarifying questions have been shown to benefit general-purpose search engines, but have not been examined in the context of code search. We present a method for generating natural-sounding clarifying questions using information extracted from function names and comments. Our method outperformed a keyword-based method for single-turn refinement in synthetic studies, and was associated with shorter search duration in human studies.
Code (1)
Tasks
Code SearchSimilar Papers 제목 키워드 기반
Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search
Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth…
Conversational SearchInformation RetrievalQuestion SelectionRepresentation Learning+1Estimating the Usefulness of Clarifying Questions and Answers for Conversational Search
While the body of research directed towards constructing and generating clarifying questions in mixed-initiative conversational search systems is vast, research aimed at processing and comprehending users' answers to suc…
Conversational SearchRetrievalZero-shot Clarifying Question Generation for Conversational Search
A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been widely studied and considered an effecti…
Conversational SearchNatural QuestionsQuestion GenerationQuestion-Generation+1AGENT-CQ: Automatic Generation and Evaluation of Clarifying Questions for Conversational Search with LLMs
Generating diverse and effective clarifying questions is crucial for improving query understanding and retrieval performance in open-domain conversational search (CS) systems. We propose AGENT-CQ (Automatic GENeration, a…
Conversational SearchRetrievalAnalyzing and Learning from User Interactions for Search Clarification
Asking clarifying questions in response to search queries has been recognized as a useful technique for revealing the underlying intent of the query. Clarification has applications in retrieval systems with different int…
Re-RankingRetrieval