Bilingual Terminology Extraction from Comparable E-Commerce Corpora

TL;DR

This paper introduces a novel framework for extracting bilingual e-commerce terminologies from comparable data, leveraging cross-lingual pre-training to improve accuracy over existing methods.

Contribution

The paper presents a new approach that utilizes cross-lingual pre-training and deep semantic relationships to extract bilingual terminologies from comparable corpora in e-commerce.

Findings

Achieves significantly better performance than strong baselines.

Effective in extracting accurate bilingual terminologies from scarce parallel data.

Applicable to various language pairs in e-commerce domain.

Abstract

Bilingual terminologies are important machine translation resources in the field of e-commerce, which are usually either manually translated or automatically extracted from parallel data. The human translation is costly and e-commerce parallel corpora is very scarce. However, the comparable data in different languages in the same commodity field is abundant. In this paper, we propose a novel framework of extracting e-commercial bilingual terminologies from comparable data. Benefiting from the cross-lingual pre-training in e-commerce, our framework can make full use of the deep semantic relationship between source-side terminology and target-side sentence to extract corresponding target terminology. Experimental results on various language pairs show that our approaches achieve significantly better performance than various strong baselines.