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Despite being the seventh most widely spoken language in the world, Bengalihas received much less attention in machine translation literature due to beinglow in resources. Most publicly available parallel corpora for Bengali are notlarge enough; and have rather poor quality, mostly because of incorrectsentence alignments resulting from erroneous sentence segmentation, and alsobecause of a high volume of noise present in them. In this work, we build acustomized sentence segmenter for Bengali and propose two novel methods forparallel corpus creation on low-resource setups: aligner ensembling and batchfiltering. With the segmenter and the two methods combined, we compile ahigh-quality Bengali-English parallel corpus comprising of 2.75 millionsentence pairs, more than 2 million of which were not available before.Training on neural models, we achieve an improvement of more than 9 BLEU scoreover previous approaches to Bengali-English machine translation. We alsoevaluate on a new test set of 1000 pairs made with extensive quality control.We release the segmenter, parallel corpus, and the evaluation set, thuselevating Bengali from its low-resource status. To the best of our knowledge,this is the first ever large scale study on Bengali-English machinetranslation. We believe our study will pave the way for future research onBengali-English machine translation as well as other low-resource languages.Our data and code are available at https://github.com/csebuetnlp/banglanmt.

Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for Bengali-English Machine Translation