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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2004.10919 |
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| _version_ | 1866909952214302720 |
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| author | Song, Shuangyong Wang, Chao |
| author_facet | Song, Shuangyong Wang, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_10919 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce Song, Shuangyong Wang, Chao Machine Learning Computation and Language I.2.7 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. |
| title | TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce |
| topic | Machine Learning Computation and Language I.2.7 |
| url | https://arxiv.org/abs/2004.10919 |