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Most statistical machine translation systems cannot translate words that areunseen in the training data. However, humans can translate many classes ofout-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 methodsfor OOV translation that use only subword information. We pose the OOVtranslation problem as a standalone task and intrinsically evaluate ourapproaches on fourteen typologically diverse languages across varying resourcelevels. Adding OOV translators to a statistical machine translation systemyields consistent BLEU gains (0.5 points on average, and up to 2.0) for allfourteen languages, especially in low-resource scenarios.

Augmenting Statistical Machine Translation with Subword Translation of Out-of-Vocabulary Words