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We assume a small POOR\u2013TGT bitext from which we learn word-level and phrase-level paraphrases and cross-lingual morphological variants between the resource-rich and the resource-poor language. Our work is of importance for resource-poor machine translation because it can provide a useful guideline for people building machine translation systems for resource-poor languages.<\/jats:p><jats:p>Our experiments for Indonesian\/Malay\u2013English translation show that using the large adapted resource-rich bitext yields 7.26 BLEU points of improvement over the unadapted one and 3.09 BLEU points over the original small bitext. Moreover, combining the small POOR\u2013TGT bitext with the adapted bitext outperforms the corresponding combinations with the unadapted bitext by 1.93\u20133.25 BLEU points. We also demonstrate the applicability of our approaches to other languages and domains.<\/jats:p>","DOI":"10.1162\/coli_a_00248","type":"journal-article","created":{"date-parts":[[2016,4,27]],"date-time":"2016-04-27T18:43:55Z","timestamp":1461782635000},"page":"277-306","source":"Crossref","is-referenced-by-count":13,"title":["Source Language Adaptation Approaches for Resource-Poor Machine Translation"],"prefix":"10.1162","volume":"42","author":[{"given":"Pidong","family":"Wang","sequence":"first","affiliation":[{"name":"Machine Zone, Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Preslav","family":"Nakov","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute, HBKU"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwee Tou","family":"Ng","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","reference":[{"key":"R1","doi-asserted-by":"crossref","unstructured":"Altintas, Kemal and Ilyas Cicekli. 2002. 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