A Hybrid Framework for Problem Solving of Comparative Questions

Abstract

Comparative questions in Chinese, as a special and complex form of question answering (QA), have their own unique sentence structure, existing methods cannot solve them well. Inspired by cognitive studies on how humans solve complex problems, we propose a hybrid framework which combines Logic Programming and attention based Bi-LSTM. This framework is decomposed into three consecutive components: 1) identify comparative questions, 2) extract comparative elements from the identified comparative questions, and 3) answer factoid questions containing the extracted comparative elements. Specifically, for the former two components, Logic Programming is adopted to filter out non-comparative questions and extract comparative elements. For the latter one, a bidirectional long and short term memory (Bi-LSTM) model with attention mechanism is utilized. Experimental results on Chinese geographical question datasets show that our proposed hybrid framework achieves outstanding performance for practical use.