INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.11, No.2, Feb. 2019

Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

Full Text (PDF, 1260KB), PP.42-49


Views:81   Downloads:10

Author(s)

Pooja Malik, Anurag Singh Baghel

Index Terms

Machine Translation;Machine Translation Evaluation;Similarity metrics;ATEC Score;Google and Bing Translators

Abstract

Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi). To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language. In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics. Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods. Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score. This is found to provide more accuracy than prevailing methods. Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.

Cite This Paper

Pooja Malik, Anurag Singh Baghel, "Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.2, pp. 42-49, 2019.DOI: 10.5815/ijmecs.2019.02.06

Reference

[1]Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]Cabrerizo, F. J., Morente-Molinera, J. A., Pedrycz, W., Taghavi, A., & Herrera-Viedma, E. (2018). Granulating linguistic information in decision making under consensus and consistency. Expert Systems with Applications, 99, 83-92.

[4]Berthiaume, R., Daigle, D., & Desrochers, A. (Eds.). (2018). Morphological Processing and Literacy Development: Current Issues and Research. Routledge.

[5]Romero-Fresco, P., & Pöchhacker, F. (2018). Quality assessment in interlingual live subtitling: The NTR Model. Linguistica Antverpiensia, New Series–Themes in Translation Studies, 16.

[6]Sárosi-Márdirosz, K. (2014). Problems related to the translation of political texts. Acta Universitatis Sapientiae, Philologica, 6(2), 159-180.

[7]Woll, N. (2018). Investigating dimensions of metalinguistic awareness: what think-aloud protocols revealed about the cognitive processes involved in positive transfer from L2 to L3. Language Awareness, 1-19.

[8]Alkhatib, M., & Shaalan, K. (2018). The Key Challenges for Arabic Machine Translation. In Intelligent Natural Language Processing: Trends and Applications (pp. 139-156). Springer, Cham.

[9]Lin, X. V., Wang, C., Zettlemoyer, L., & Ernst, M. D. (2018). NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System. arXiv preprint arXiv:1802.08979.

[10]Ciobanu, D. (2018). Collaborative Student Translation Projects. Multilingual Writing and Pedagogical Cooperation in Virtual Learning Environments, 222.

[11]Mollo, G., Jefferies, E., Cornelissen, P., & Gennari, S. P. (2018). Context-dependent lexical ambiguity resolution: MEG evidence for the time-course of activity in left inferior frontal gyrus and posterior middle temporal gyrus. Brain and language, 177, 23-36.

[12]Chen, K., Zhao, T., Yang, M., Liu, L., Tamura, A., Wang, R. & Sumita, E. (2018). A Neural Approach to Source Dependence Based Context Model for Statistical Machine Translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(2), 266-280.

[13]Berger, A., & Lafferty, J. (2017, August). Information retrieval as statistical translation. In ACM SIGIR Forum (Vol. 51, No. 2, pp. 219-226). ACM.

[14]Mallinson, J., Sennrich, R., & Lapata, M. (2017). Paraphrasing revisited with neural machine translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (Vol. 1, pp. 881-893).

[15]Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. arXiv preprint arXiv:1706.03872.

[16]Zhang, H., Li, J., Ji, Y., & Yue, H. (2017). Understanding subtitles by character-level sequence-to-sequence learning. IEEE Transactions on Industrial Informatics, 13(2), 616-624.

[17]Wijaya, D. T., Callahan, B., Hewitt, J., Gao, J., Ling, X., Apidianaki, M., & Callison-Burch, C. (2017). Learning Translations via Matrix Completion. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 1452-1463).

[18]Kong, J., Yang, Y., Wang, L., Zhou, X., Jiang, T., & Li, X. (2017). Filtering Reordering Table Using a Novel Recursive Autoencoder Model for Statistical Machine Translation. Mathematical Problems in Engineering, 2017.

[19]Cox, G., Yan, Z., Bhattacharjee, A., & Ganapathy, V. (2018). Secure, Consistent, and High-Performance Memory Snapshotting.