Supervised Classification Based Machine Translation Quality Estimation
Nivedita Bharti1, Nisheeth Joshi2, Iti Mathur3
1Nivedita Bharti*, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
2Nisheeth Joshi, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
3Iti Mathur, Department of Computer Science, Banasthali Vidyapith, Rajasthan, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2697-2703 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6029029320/2020©BEIESP | DOI: 10.35940/ijeat.C6029.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This submission describes the study of linguistically motivated features to estimate the translated sentence quality at sentence level on English-Hindi language pair. Several classification algorithms are employed to build the Quality Estimation (QE) models using the extracted features. We used source language text and the MT output to extract these features. Experiments show that our proposed approach is robust and producing competitive results for the DT based QE model on neural machine translation system.
Keywords: Machine Translation, Quality Estimation, Classification Algorithms, Features, Performance Evaluation.