Toward a Test Set of Dislocations in Persian for Neural Machine Translation

International audience This paper describes a test set designed to analyse the translation of dislocations from Persian, to be used for testing neural machine translation models. We first tested the accuracy of the two Universal dependency treebanks for Persian to automatically detect dislocations. Then we parsed the available Persian treebanks on GREW (Bonfante et al., 2018) to build a specific test set containing examples of dislocations. With available aligned data on OPUS (Tiedemann, 2016), we trained a model to translate from Persian into English on openNMT (Klein et al., 2017). We report the results of our translation test set by several toolkits (Google Translate, MBART-50 (Tang et al., 2020), Microsoft Bing and our in-house translation model) for the translation into English. We discuss why dislocations in Persian provide an interesting testbed for neural machine translation.