Journal of East China Normal University(Natural Science) >

2019 , Vol. 2019 >Issue 5: 113 - 122,167

DOI: https://doi.org/10.3969/j.issn.1000-5641.2019.05.009

Computational Intelligence in Emergent Applications

Self-attention based neural networks for product titles compression

  • FU Yu ,
  • LI You ,
  • LIN Yu-ming ,
  • ZHOU Ya
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  • Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin Guangxi 541004, China

Received date: 2019-07-28

  Online published: 2019-10-11

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Abstract

E-commerce product title compression has received significant attention in recent years, since it can facilitate more specific information for cross-platform knowledge alignment and multi-source data fusion. Product titles usually contain redundant descriptions, which can lead to inconsistencies. In this paper, we propose self-attention based neural networks for this task. Given the fact that self-attention mechanism networks cannot directly capture sequence features of product names, we enhance the mapping networks with a dot-attention structure, which was computed for the query and key-value pairs by a gated recurrent unit (GRU) based recurrent neural network. The proposed method improves the analytical capability of the model at a lower relative computational cost. Based on data from LESD4EC, we built two E-commerce datasets of product core phrases named LESD4EC L and LESD4EC S; we subsequently tested the model on these two datasets. A series of experiments show that the proposed model achieves better performance in product title compression than existing techniques.

Key words： self-attention mechanism; product titles compression; gated recurrent units

Cite this article

FU Yu , LI You , LIN Yu-ming , ZHOU Ya . Self-attention based neural networks for product titles compression[J]. Journal of East China Normal University(Natural Science), 2019 , 2019(5) : 113 -122,167 . DOI: 10.3969/j.issn.1000-5641.2019.05.009

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