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

Source: [TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce](<https://www.sota2.com/research/paper/68fa67721e209131cea164fb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-12-12T05:01:30.602Z

Authors: Shuangyong Song, Chao Wang

Published: 2020-04-23T01:02:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.10919>)

[PDF](<https://arxiv.org/pdf/2004.10919.pdf>)