Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora

Proceedings of the 28th International Conference on Computational Linguistics, pages 6002–6012 Barcelona, Spain (Online), December 8-13, 2020 6002 Data Selection for Bilingual Lexicon Induction from Specialized Comparable Corpora Martin Laville1, Amir Hazem1, Emmanuel Morin1, Philippe Langlais2 1LS2N, UMR CNRS 6004, Universite de Nantes, France 2RALI-DIRO, Montreal, Canada 1firstname.lastname@ls2n.fr, 2felipe@iro.umontreal.ca Abstract Narrow specialized comparable corpora are often small in size. This particularity makes it diffi- cult to build efficient models to acquire translation equivalents, especially for less frequent and rare words. One way to overcome this issue is to enrich the specialized corpora with out-of- domain resources. Although some recent studies have shown improvements using data augmen- tation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally. In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora. We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy. Then, we propose to exploit BERT for data selection. Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin. The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10. 1 Introduction Mining translation equivalents to automate the process of generating and extending dictionaries from bilingual corpora is a well known Natural Language Processing (NLP) application. Initially conducted on parallel data, this modus operandi has rapidly moved to using comparable corpora, mainly due to their availability and the fact that they are easier to acquire (Sharoff et al., 2013). Comparable corpora gather texts of the same domain, often over the same period without being in a translation relation. Journalistic or encyclopedic websites as well as web crawl data are usually popular for this purpose (Rapp, 1999; Li and Gaussier, 2010; Rapp et al., 2020). In technical and scientific domains, comparable corpora suffer from their modest and limited size compared to general language corpora. This phenomenon is amplified by the difficulty to obtain many specialized documents in a language other than English (Morin and Hazem, 2014). This aspect, which is gradually changing with the deployment of open access data, is nevertheless a major difficulty. One way to overcome the limited size of specialized (in-domain) comparable corpora for bilingual lexicon induction (BLI) is to associate them with out-of-domain resources as lexical databases (Bouamor et al., 2013) or large general domain corpora (Hazem and Morin, 2018). For instance, by combining a specialized comparable corpus with a general domain corpus, methods based on distributional analysis are boosted (Hazem and Morin, 2018). General domain corpora greatly enhance the representation of specialized vocabulary by adding new contexts. The main drawbacks of this data augmentation approach is the introduction of polysemous information as well as the tremendous increase of computation time. Consider for instance, a French/English comparable corpus in the medical domain and the French …

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