MTIL2017: Machine translation using recurrent neural network on statistical machine translation

46Citations
Citations of this article
66Readers
Mendeley users who have this article in their library.

Abstract

Machine translation (MT) is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks (RNNs) over traditional statistical MT (SMT). We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have constructed the traditional MT model using Moses toolkit and have additionally enriched the language model using external data sets. Thereafter, we have ranked the phrase tables using an RNN encoder-decoder module created originally as a part of the GroundHog project of LISA lab.

Cite

CITATION STYLE

APA

Mahata, S. K., Das, D., & Bandyopadhyay, S. (2019). MTIL2017: Machine translation using recurrent neural network on statistical machine translation. Journal of Intelligent Systems, 28(3), 447–453. https://doi.org/10.1515/jisys-2018-0016

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free