With the prosperous of cross-border e-commerce, there is an urgent demand for
designing intelligent approaches for assisting e-commerce sellers to offer
local products for consumers from all over the world. In this paper, we explore
a new task of cross-lingual information retrieval, i.e., cross-lingual
set-to-description retrieval in cross-border e-commerce, which involves
matching product attribute sets in the source language with persuasive product
descriptions in the target language. We manually collect a new and high-quality
paired dataset, where each pair contains an unordered product attribute set in
the source language and an informative product description in the target
language. As the dataset construction process is both time-consuming and
costly, the new dataset only comprises of 13.5k pairs, which is a low-resource
setting and can be viewed as a challenging testbed for model development and
evaluation in cross-border e-commerce. To tackle this cross-lingual
set-to-description retrieval task, we propose a novel cross-lingual matching
network (CLMN) with the enhancement of context-dependent cross-lingual mapping
upon the pre-trained monolingual BERT representations. Experimental results
indicate that our proposed CLMN yields impressive results on the challenging
task and the context-dependent cross-lingual mapping on BERT yields noticeable
improvement over the pre-trained multi-lingual BERT model.

本文探讨在跨境电商中，通过跨语言信息检索的方式，将源语言中商品属性集与目标语言中的诱人描述相匹配的问题。我们人工收集了一个新的、高质量的配对数据集，通过该数据集构建了一个新的跨语言匹配网络（CLMN），在 BERT 预训练模型的基础上增强了上下文相关的跨语言映射。实验结果表明，我们的 CLMN 处理这个具有挑战性的任务的效果非常显著，而针对 BERT 中上下文相关的跨语言映射则对性能提升明显。