Paper: Source Language Adaptation for Resource-Poor Machine Translation

ACL ID D12-1027
Title Source Language Adaptation for Resource-Poor Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012

We propose a novel, language-independent approach for improving machine translation from a resource-poor language to X by adapt- ing a large bi-text for a related resource-rich language and X (the same target language). We assume a small bi-text for the resource- poor language to X pair, which we use to learn word-level and phrase-level paraphrases and cross-lingual morphological variants be- tween the resource-rich and the resource-poor language; we then adapt the former to get closer to the latter. Our experiments for Indonesian/Malay?English translation show that using the large adapted resource-rich bi- text yields 6.7 BLEU points of improvement over the unadapted one and 2.6 BLEU points over the original small bi-text. Moreover, combining the small bi-text with the adapted bi-text out...