论文标题
TCNN：电子商务中基于检索的问题回答系统的三重卷积神经网络模型
TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
论文作者
论文摘要
在过去的几年中，自动提问（QA）系统蓬勃发展，常用的技术可以大致分为信息检索（IR）基于（IR）的基于生成和生成。基于IR的模型的一个关键解决方案是从质量检查知识库中检索给定查询的最相似知识条目，然后重新使用具有语义匹配模型的知识条目。在本文中，我们旨在通过提出的文本匹配模型（包括基本的三重卷积神经网络（TCNN）模型）和两个基于注意力的TCNN（ATCNN）模型来改善基于IR的电子商务质量商务QA System-lime。实验结果表明它们的作用。
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.
