Experimental evaluation reveals improved translation quality in English-Malay statistical machine translation, indicating reduced out-of-vocabulary errors using dictionaries and lemmatization.
Statistical Machine Translation (SMT) is one of the most popular methods for machine translation. In this work, we carried out English-Malay SMT by acquiring an English-Malay parallel corpus in computer science domain. On the other hand, the training parallel corpus is from a general domain. Thus, there will be a lot of out of vocabulary during translation. We attempt to improve the English-Malay SMT in computer science domain using a dictionary and an English lemmatizer. Our study shows that a combination of approach using bilingual dictionary and English lemmatization improves the BLEU score for English to Malay translation from 12.90 to 15.41.
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Yeong et al. (2016) studied this question.
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