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Bilingual Lexicon Induction across Orthographically-distinctUnder-Resourced Dravidian Languages

Authors: 

Bharathi Raja, Navaneethan Rajasekaran, Mihael Arcan, Kevin McGuinness, Noel O’Connor, John McCrae

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Bilingual lexicons are a vital tool for under-resourced languages and recent state-of-the-art approaches to this leverage pretrained monolingual word embeddings using supervised or semi-supervised approaches. However, these approaches require cross-lingual information such as seed dictionaries to train the model and find a linear transformation between the word embedding spaces. Especially in the case of low-resourced languages, seed dictionaries are not readily available, and as such, these methods produce extremely weak results on these languages. In this work, we focus on the Dravidian languages, namely Tamil, Telugu, Kannada, and Malayalam, which are even more challenging as they are written in unique scripts. To take advantage of orthographic information and cognates in these languages, we bring the related languages into a single script. Previous approaches have used linguistically sub-optimal measures such as the Levenshtein edit distance to detect cognates, whereby we demonstrate that the longest common sub-sequence is linguistically more sound and improves the performance of bilingual lexicon induction. We show that our approach can increase the accuracy of bilingual lexicon induction methods on these languages many times, making bilingual lexicon induction approaches feasible for such under-resourced languages.
Proceedings: 
Proceedings of the Seventh Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2020)
Digital Object Identifer (DOI): 
10.5281/zenodo.4320725
Publication Date: 
06/12/2020
Research Group: 
Institution: 
Dublin City University (DCU)
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes