Improving Translation Quality By Using Ensemble Approach

Authors

  • D. Chopra Department of Computer Science, Banasthali University, India
  • N. Joshi Department of Computer Science, Banasthali University, India
  • I. Mathur Department of Computer Science, Banasthali University, India
Volume: 8 | Issue: 6 | Pages: 3512-3514 | December 2018 | https://doi.org/10.48084/etasr.2269

Abstract

Machine translation (MT) has been a topic of great research during the last sixty years, but, improving its quality is still considered an open problem. In the current paper, we will discuss improvements in MT quality by the use of the ensemble approach. We performed MT from English to Hindi using 6 MT different engines described in this paper. We found that the quality of MT is improved by using a combination of various approaches as compared to the simple baseline approach for performing MT from source to target text.

Keywords:

machine translation, named entity translation, natural language processing, source text rewriting

Downloads

Download data is not yet available.

References

R. Srivastava, R. A. Bhat, “Transliteration systems across indian languages using parallel corpora”, 27th Pacific Asia Conference on Language, Information, and Computation, pp. 390-398, Taiwan, November 21-24, 2013

V. H. Yngve, “The machine and the man”, Mechanical Translation, Vol. 1, No. 2, pp. 20-22, 1954

B. Vauquois, “A survey of formal grammars and algorithms for recognition and transformation in machine translation”, IFIP Congress (2), Vol. 68, pp. 1114-1122, UK, August 5-10, 1968

W. Weaver, “Translation”, in: Machine Translation of Languages, pp. 15-23, 1955

N. Joshi, I. Mathur, H. Darbari, A. Kumar, “HEval: Yet another human evaluation metric”, International Journal on Natural Language Computing, Vol. 2, No. 5, pp. 21-36, 2013 DOI: https://doi.org/10.5121/ijnlc.2013.2502

Downloads

How to Cite

[1]
D. Chopra, N. Joshi, and I. Mathur, “Improving Translation Quality By Using Ensemble Approach”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 6, pp. 3512–3514, Dec. 2018.

Metrics

Abstract Views: 524
PDF Downloads: 386

Metrics Information

Most read articles by the same author(s)