An Enhanced Convolutional Inference Model with Distillation for Retrieval-Based QA

Shuangyong Song, Chao Wang, Xiao Pu, Zehui Wang, Huan Chen

Abstract: A common solution of automatic question-answering (QA) systems is retrieving the most similar question for a given user query from a QA knowledge base. Even though some models have got promising performance on this task, it may be hard for them to achieve a balance between accuracy and efficiency. In this paper, we propose an enhanced convolutional inference model with StructBert distillation, called StructBert-ECIM, to achieve such balance.