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

• URL: http://arxiv.org/abs/2004.10919v1
• Date: Thu, 23 Apr 2020 01:02:15 GMT
• Title: TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
• Authors: Shuangyong Song, Chao Wang
• Abstract summary: 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.
• Score: 6.1786972717541895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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