电子商务中检索式问答系统的TCNN（三卷积神经网络）模型（CS LG）---Elva

近年来，自动问答系统蓬勃发展，常用的技术大致可分为基于信息检索（Information Retrieval (IR)-based）和基于生成（generation-based）两大类。基于信息检索的模型的一个关键解决方案是从问答知识库中检索与给定查询最相似的知识条目，然后用语义匹配模型对这些知识条目进行重新排序。本文针对基于信息检索的电子商务问答系统AliMe提出了文本匹配模型，模型中包含一个基本的三卷积神经网络（TCNN）模型和两个基于注意力的神经网络（ATCNN）模型，我们的实验结果显示出了它们的效果。

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

原文：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.

原文作者：Shuangyong Song, Chao Wang

原文链接：https://arxiv.org/abs/2004.10919