تقرير
Bilingual Terminology Extraction from Comparable E-Commerce Corpora
| العنوان: | Bilingual Terminology Extraction from Comparable E-Commerce Corpora |
|---|---|
| المؤلفون: | Jia, Hao, Gu, Shuqin, Zhang, Yuqi, Duan, Xiangyu |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language |
| الوصف: | 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. Comment: Accepted by the 2022 International Joint Conference on Neural Networks (IJCNN 2022) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2104.07398 |
| رقم الأكسشن: | edsarx.2104.07398 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |