TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
This work addresses enhancing QA systems for e-commerce users, but it appears incremental as it builds on existing IR-based methods with new model variants.
The authors tackled improving retrieval-based question answering in e-commerce by proposing Triple Convolutional Neural Network (TCNN) and Attention-based TCNN models, with experimental results showing their effectiveness.
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.