  Prepositions are very common and very ambiguous, and understanding their
sense is critical for understanding the meaning of the sentence. Supervised
corpora for the preposition-sense disambiguation task are small, suggesting a
semi-supervised approach to the task. We show that signals from unannotated
multilingual data can be used to improve supervised preposition-sense
disambiguation. Our approach pre-trains an LSTM encoder for predicting the
translation of a preposition, and then incorporates the pre-trained encoder as
a component in a supervised classification system, and fine-tunes it for the
task. The multilingual signals consistently improve results on two
preposition-sense datasets.
