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Tracing Source Language Interference in Translation with Graph-Isomorphism Measures

Koel Dutta Chowdhury; Cristina España-Bonet; Josef van Genabith
In: Ruslan Mitkov; Galia Angelova (Hrsg.). Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2021. Recent Advances in Natural Language Processing (RANLP-2021), Varna, Bulgaria, INCOMA Ltd. 9/2021.


Previous research has used linguistic features to show that translations exhibit traces of source language interference and that phylogenetic trees between languages can be reconstructed from the results of translations into the same language. Recent research has has been shown that instances of translationese (source language interference) can even be detected in embedding spaces, comparing embeddings spaces of original language data with embedding spaces resulting from translations into the same language, using a simple Eigenvector-based divergence from isomorphism measure. To date it remains an open question whether alternative graph-isomorphism measures can produce better results. In this paper, we (i) explore Gromov-Hausdorff distance, (ii) present a novel spectral version of the Eigenvector-based method, and (iii) evaluate all approaches against a broad linguistic typological database (URIEL). We show that language distances resulting from our spectral isomorphism approaches can reproduce genetic trees at par with previous work without requiring any explicit linguistic information and that the results can be extended to non-Indo-European languages. Finally, we show that the methods are robust under a variety of modeling conditions.