  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 even on the level of 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.
