  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.
