Improved compare-aggregate model for Chinese document-based question answering

Abstract

Document-based question answering (DBQA) is a sub-task in question answering. It aims to measure the matching relation between questions and answers, which can be regarded as sentence matching problem. In this paper, we introduce a Compare-Aggregate architecture to handle the word-level comparison and aggregation. To deal with the noisy information in traditional attention mechanism, the k-top attention mechanism is proposed to filter out irrelevant words. Subsequently, we propose a combined model to merge matching relation learned by Compare-Aggregate model with shallow features to generate the final matching score. We evaluate our model on Chinese Document-based Question Answering (DBQA) task. The experimental results show the effectiveness of our proposed improved methods. And our final combined model achieves second place result on the DBQA task of NLPCC-ICCPOL 2017 Shared Task. The paper provides the technical details of the proposed algorithm.