Reusing Keywords for Fine-grained Representations and Matchings

Li Chong, Denghao Ma, Yueguo Chen, Xueqiang Lv

Abstract: Question retrieval aims to find semantically equivalent questions for an exemplary question, suffering from a key challenge — lexical gap. Previous solutions mainly focus on utilizing translation models, topic models and deep learning techniques to perform global matching. Different from the previous solutions, we propose new insights of reusing important keywords to construct fine-grained semantic representations of questions and then fine-grained matchings for two questions, which will inspire future research to explore and mine new solutions from the questions themselves. To realize these insights, we propose a practical fine-grained matching network with two cascaded units: (i) fine-grained representation unit, which uses multi-level keyword sets to represent question semantics of different granularity; (ii) fine-grained matching unit, which performs matchings in multiple granularities (to achieve both global matching and local matching) and multiple views (to achieve both lexical matching and semantic matching). We conduct extensive experiments on three public datasets and the experimental results show that our proposed model outperforms the state-of-the-art solutions.