Bilingual Terminology Extraction from Comparable E-Commerce Corpora

被引:1
作者
Jia, Hao [1 ]
Gu, Shuqin [2 ]
Zhang, Yuqi [2 ]
Duan, Xiangyu [1 ]
机构
[1] Soochow Univ, Sch Comp Sci & Technol, Inst Aritificial Intelligence, Suzhou, Peoples R China
[2] Alibaba DAMO Acad, Machine Intelligence Technol Lab, Hangzhou, Peoples R China
来源
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2022年
关键词
bilingual terminology extraction; e-commerce domain; cross-lingual pre-training;
D O I
10.1109/IJCNN55064.2022.9892544
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:8
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