Translation of Unknown Words in Low Resource Languages

We address the problem of unknown words, also known as out of vocabulary (OOV) words, in machine translation of low resource languages. Our technique comprises a combination of methods, inspired by the common OOV types observed. We also design evaluation techniques for measuring coverage of OOVs achieved and integrate the new translation candidates in a Statistical Machine Translation (SMT) system. Experimental results on Hindi and Uzbek show that our system achieves a good coverage of OOV words. We show that our methods produced correct candidates for 50% of Hindi OOVs and 30% of Uzbek OOVs, in scenarios that have 1 and 3 OOVs per sentence. This offers a potential for improvement of translation quality for languages that have limited parallel data available for training.