Eva Vanmassenhove


A Case Study of Natural Gender Phenomena in Translation: A Comparison of Google Translate, Bing Microsoft Translator and DeepL for English to Italian, French and Spanish
Argentina Anna Rescigno | Johanna Monti | Andy Way | Eva Vanmassenhove
Workshop on the Impact of Machine Translation (iMpacT 2020)


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Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
Eva Vanmassenhove | Dimitar Shterionov | Andy Way
Proceedings of Machine Translation Summit XVII Volume 1: Research Track


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Getting Gender Right in Neural Machine Translation
Eva Vanmassenhove | Christian Hardmeier | Andy Way
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such difference is related to the way gender is expressed in a language. Saying “I am happy” in English, does not encode any additional knowledge of the speaker that uttered the sentence. However, many other languages do have grammatical gender systems and so such knowledge would be encoded. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained/recovered. The same sentence would become either “Je suis heureux”, for a male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or syntactic constructions (Tannen, 1991; Pennebaker et al., 2003). We integrate gender information into NMT systems. Our contribution is two-fold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.

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SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags
Eva Vanmassenhove | Andy Way
Proceedings of ACL 2018, Student Research Workshop

In this paper we incorporate semantic supersensetags and syntactic supertag features into EN–FR and EN–DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according to the BLEU scores.