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Register a new userBilingual Terminology Extraction from Non-Parallel E-Commerce Corpora
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Added by
Hao Jia
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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.
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Parallel texts are a relatively rare language resource, however, they constitute a very useful research material with a wide range of applications. This study presents and analyses new methodologies we developed for obtaining such data from previously built comparable corpora. The methodologies are automatic and unsupervised which makes them good for large scale research. The task is highly practical as non-parallel multilingual data occur much more frequently than parallel corpora and accessing them is easy, although parallel sentences are a considerably more useful resource. In this study, we propose a method of automatic web crawling in order to build topic-aligned comparable corpora, e.g. based on the Wikipedia or Euronews.com. We also developed new methods of obtaining parallel sentences from comparable data and proposed methods of filtration of corpora capable of selecting inconsistent or only partially equivalent translations. Our methods are easily scalable to other languages. Evaluation of the quality of the created corpora was performed by analysing the impact of their use on statistical machine translation systems. Experiments were presented on the basis of the Polish-English language pair for texts from different domains, i.e. lectures, phrasebooks, film dialogues, European Parliament proceedings and texts contained medicines leaflets. We also tested a second method of creating parallel corpora based on data from comparable corpora which allows for automatically expanding the existing corpus of sentences about a given domain on the basis of analogies found between them. It does not require, therefore, having past parallel resources in order to train a classifier.
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