  Bilingual terminologies are important resources for natural language
processing (NLP) applications. The acquisition of bilingual terminology pairs
is either human translation or automatic extraction from parallel data. We
notice that comparable corpora could also be a good resource for extracting
bilingual terminology pairs, especially for e-commerce domain. The parallel
corpora are particularly scarce in e-commerce settings, but the non-parallel
corpora in different languages from the same domain are easily available. In
this paper, we propose a novel framework of extracting bilingual terminologies
from non-parallel comparable corpus in e-commerce. Benefiting from
cross-lingual pre-training in e-commerce, our framework can extract the
corresponding target terminology by fully utilizing the deep semantic
relationship between source-side terminology and target-side sentence.
Experimental results on various language pairs show that our approaches achieve
significantly better performance than various strong baselines.
