Automatic question-answering (QA) systems have boomed during last few years, and commonly used techniques can be roughly categorized into Information Retrieval (IR)-based and generation-based. A key solution to the IR based 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. Experimental results show their effect.
@article{arxiv.2004.10919,
title = {TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce},
author = {Shuangyong Song and Chao Wang},
journal= {arXiv preprint arXiv:2004.10919},
year = {2025}
}