Literature Survey: Study of Neural Machine Translation
نویسندگان
چکیده
We build Neural Machine Translation (NMT) systems for EnglishHindi,Bengali-Hindi and Gujarati-Hindi with two different units of translation i.e. word and subword and present a comparative study of subword NMT and word level NMT systems, along with strong results and case studies. We train attention-based encoder-decoder model for word level and use Byte Pair Encoding (BPE) in subword NMT for word segmentation. We conduct case studies to study the effects of BPE. Since the NMT approach is a data driven approach, it suffers a lot by resource scarcity. This report also covers the Multitask learning which is an approach of transfer learning or inductive transfer. MultiTask Learning helps the learner to improve generalization performance by adding extra related tasks to the backpropagation net. The nub behind adding extra related tasks is domain specific information contained in the training signals of other tasks helps to learn shared feature of the main task better. We explained Multiway multilingual model which is based on the MTL approach which learns the translation of several Indian language pairs in parallel. We also covers the performance gained by Multi-way multilingual neural machine translation in contrast with single pair neural machine translation.
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