TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce

Shuangyong Song, Chao Wang, Haiqing Chen, Huan Chen

Abstract: A key solution to the Information Retrieval (IR)-based question answering (QA) models is to retrieve the most similar knowledge entries of a given query from a QA knowledge base, and then rerank those knowledge entries with semantic matching models. In this paper, we aim to improve an IR based e-commerce QA system-AliMe with proposed text matching models, including a basic Triple Convolutional Neural Network (TCNN) model and two Attention-based TCNN (ATCNN) models.