Neural Machine Translation (NMT) is an ongoing technique for Machine Translation (MT) using enormous artificial neural network. It has exhibited promising outcomes and has shown incredible potential in solving challenging machine translation exercises. One such exercise is the best approach to furnish great MT to language sets with a little preparing information. In this work, Zero Shot Translation (ZST) is inspected for a low resource language pair. By working on high resource language pairs for which benchmarks are available, namely Spanish to Portuguese, and training on data sets (Spanish-English and English-Portuguese) we prepare a state of proof for ZST system that gives appropriate results on the available data. Subsequently the same architecture is tested for Sanskrit to Hindi translation for which data is sparse, by training the model on English-Hindi and Sanskrit-English language pairs. In order to prepare and decipher with ZST system, we broaden the preparation and interpretation pipelines of NMT seq2seq model in tensorflow, incorporating ZST features. Dimensionality reduction of word embedding is performed to reduce the memory usage for data storage and to achieve a faster training and translation cycles. In this work existing helpful technology has been utilized in an imaginative manner to execute our NLP issue of Sanskrit to Hindi translation. A Sanskrit-Hindi parallel corpus of 300 is constructed for testing. The data required for the construction of parallel corpus has been taken from the telecasted news, published on Department of Public Information, state government of Madhya Pradesh, India website.
- Pub Date:
- June 2020
- Computer Science - Computation and Language;
- Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, December 2019, pp 377 to 383