Abstract
We present MonoTrans, a statistical machine translation system which only uses monolingual source language and target language data, without using any parallel corpora or language-specific rules. It translates each source word by the most similar target word, according to a combination of a string similarity measure and a word frequency similarity measure.
It is designed for translation between very close languages, such as Czech and Slovak or Danish and Norwegian. It provides a low-quality translation in resource-poor scenarios where parallel data, required for training a high-quality translation system, may be scarce or unavailable.
This is useful e.g. for cross-lingual NLP, where a trained model may be transferred from a resource-rich source language to a resource-poor target language via machine translation. We evaluate MonoTrans both intrinsically, using BLEU, and extrinsically, applying it to cross-lingual tagger and parser transfer.
Although it achieves low scores, it does surpass the baselines