2021/01/20 by Deepak Gupta, Gupta, Deepak, Rajkumar Pujari +12
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Natural Language Processing Techniques #Text and Document Classification Technologies #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2101.08201
Paper was accepted at COLING 2018, presented as a poster
arxiv created 2021/01/20 · openalex publication_date 2021/01/20 · arxiv updated 2021/01/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we propose a hybrid technique for semantic question matching. It uses our proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning (DL) or taxonomy-based knowledge alone.