  Most statistical machine translation systems cannot translate words that are
unseen in the training data. However, humans can translate many classes of
out-of-vocabulary (OOV) words (e.g., novel morphological variants,
misspellings, and compounds) without context by using orthographic clues.
Following this observation, we describe and evaluate several general methods
for OOV translation that use only subword information. We pose the OOV
translation problem as a standalone task and intrinsically evaluate our
approaches on fourteen typologically diverse languages across varying resource
levels. Adding OOV translators to a statistical machine translation system
yields consistent BLEU gains (0.5 points on average, and up to 2.0) for all
fourteen languages, especially in low-resource scenarios.
