Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce

Abstract

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.

Cross-language Information Retrieval

“…Nair et al [122] compared early and late fusion using two approaches (one with language model context, one without), finding similar improvements over the best single system from both approaches. Finally, Li et al [92] experimented with using cross-attention for early fusion between CLIR (for product descriptions) and product attribute matching in an e-commerce application.…”

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

“…The escalation of globalization burgeons the great demand for Cross-Lingual Information Retrieval (CLIR), which has broad applications such as cross-border e-commerce, crosslingual question answering, and so on (Li et al 2020;Rücklé, Swarnkar, and Gurevych 2019;Xu et al 2021). Informally, given a query in one language, CLIR is a document retrieval task that aims to rank the candidate documents in another language according to the relevance between the search query and the documents.…”

“…Recent studies strive to model CLIR with deep neural networks that encode both query and document into a shared space rather than using MT systems (Zhang et al 2019;Sasaki et al 2018;Hui et al 2018a;Li et al 2020). Though these approaches achieve some remarkable successes, the intrinsic differences between different languages still exist due to the implicit alignment of these methods.…”

Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement

“…The escalation of globalization burgeons the great demand for Cross-Lingual Information Retrieval (CLIR), which has broad applications such as cross-border e-commerce, crosslingual question answering, and so on (Li et al 2020;Rücklé, Swarnkar, and Gurevych 2019;Xu et al 2021). Informally, given a query in one language, CLIR is a document retrieval task that aims to rank the candidate documents in another language according to the relevance between the search query and the documents.…”

“…Recent studies strive to model CLIR with deep neural networks that encode both query and document into a shared space rather than using MT systems (Zhang et al 2019;Sasaki et al 2018;Hui et al 2018b;Li et al 2020). Though these approaches achieve some remarkable successes, the intrinsic differences between different languages still exist due to the implicit alignment of these methods.…”