K2Q: Generating Natural Language Questions from Keywords with User Refinements
Abstract
Garbage in and garbage out. A Q&A system must receive a well formulated question that matches the user’s intent or she
has no chance to receive satisfactory answers. In this paper, we propose a keywords to questions (K2Q) system to assist a user to articulate and refine questions.
K2Q generates candidate questions and refinement words from a set of input keywords. After specifying some initial keywords, a user receives a list of candidate questions as well as a list of refinement words. The user can then select a satisfactory question, or select a refinement word
to generate a new list of candidate questions and refinement words. We propose a User Inquiry Intent (UII) model to de-
scribe the joint generation process of keywords and questions for ranking questions, suggesting refinement words, and generating questions that may not have previously
appeared. Empirical study shows UII to be useful and effective for the K2Q task.
has no chance to receive satisfactory answers. In this paper, we propose a keywords to questions (K2Q) system to assist a user to articulate and refine questions.
K2Q generates candidate questions and refinement words from a set of input keywords. After specifying some initial keywords, a user receives a list of candidate questions as well as a list of refinement words. The user can then select a satisfactory question, or select a refinement word
to generate a new list of candidate questions and refinement words. We propose a User Inquiry Intent (UII) model to de-
scribe the joint generation process of keywords and questions for ranking questions, suggesting refinement words, and generating questions that may not have previously
appeared. Empirical study shows UII to be useful and effective for the K2Q task.
Research Areas
