Machine Learning Approaches for Bangla Statistical Machine Translation

Machine Learning Approaches for Bangla Statistical Machine Translation

Maxim Roy
DOI: 10.4018/978-1-4666-3970-6.ch004
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Machine Translation (MT) from Bangla to English has recently become a priority task for the Bangla Natural Language Processing (NLP) community. Statistical Machine Translation (SMT) systems require a significant amount of bilingual data between language pairs to achieve significant translation accuracy. However, being a low-density language, such resources are not available in Bangla. In this chapter, the authors discuss how machine learning approaches can help to improve translation quality within as SMT system without requiring a huge increase in resources. They provide a novel semi-supervised learning and active learning framework for SMT, which utilizes both labeled and unlabeled data. The authors discuss sentence selection strategies in detail and perform detailed experimental evaluations on the sentence selection methods. In semi-supervised settings, reversed model approach outperformed all other approaches for Bangla-English SMT, and in active learning setting, geometric 4-gram and geometric phrase sentence selection strategies proved most useful based on BLEU score results over baseline approaches. Overall, in this chapter, the authors demonstrate that for low-density language like Bangla, these machine-learning approaches can improve translation quality.
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Semi-Supervised Learning

Semi-supervised learning refers to the use of both labeled and unlabeled data for training. Semi-supervised learning techniques can be applied to SMT when a large amount of bilingual parallel data is not available for language pairs. Sarkar, Haffari, and Ueffing (2007) explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation accuracy.

Self-training is a commonly used technique for semi-supervised learning. In self-training a classifier is first trained with a small amount of labeled data. The classifier is then used to classify the unlabeled data. Typically, the most confident unlabeled points, together with their predicted labels, are added to the training set. The classifier is retrained and the procedure repeated. Note the classifier uses its own predictions to teach itself. The procedure is also called self-teaching or bootstrapping.

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