  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.
