TCNN：用于电商基于检索的问答系统中的三卷积神经网络模型
TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
AI总结：
本文提出TCNN和ATCNN模型，用于改进电商基于检索的问答系统，通过文本匹配提升问答性能。
摘要：
自动问答系统在近几年迅速发展，常用的技巧大致分为基于信息检索（IR）和生成式两种。基于IR模型的关键解决方案是从QA知识库中检索出给定查询的最相似知识条目，然后使用语义匹配模型对这些知识条目进行重新排序。本文旨在改进一个基于IR的电商问答系统-AliMe，提出文本匹配模型，包括一个基本的三卷积神经网络（TCNN）模型和两个基于注意力的TCNN（ATCNN）模型。实验结果展示了它们的效果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
