Interpretation and machine translation towards google translate as a part of machine translation and teaching translation

Vichard L. Kane

doi:10.51708/apptrans.v15n1.1337

Authors

Vichard L. Kane
kane@nyu.edu
New York University, New York, United States

Keywords:

English, google translate, interpretation, machine translation, multilanguage

Abstract

Language comprehension is the capacity of someone to properly understand the language to fully communicate the message and details. When dialects are distinct, the problem arises. This condition can lead to misconception as understudies, particularly those whose specialty is not English, cannot gain real importance. Along these lines, interpreting is regarded as one of the suggested arrangements in this area. As the results, the message's basic significance and setting in an unknown dialect can be precisely seen in English. Interpretation is a help to resolve this language boundary for this case. Moreover, finding an individual who is accessible to decipher each and every language is found troublesome. Furthermore, the aftereffect of interpretation is by one way or another influenced and impacted by the interpreter's abilities. In this manner, interpretation application turns into the one to be depended on. A lot of online interpretation applications have been made available for the last few years. The best one is Google Translate which is a multi-lingual online computer interpretation (MT) system. It is said as a multilanguage interpretation programme, as it can decode material from over 90 dialects.

References

Amancio, D. R., Nunes, M. D. G., Oliveira Jr, O. N., Pardo, T. A. S., Antiqueira, L., & Costa, L. D. F. (2011). Using metrics from complex networks to evaluate machine translation. Physica A: Statistical Mechanics and its Applications, 390(1), 131-142. https://doi.org/10.1016/j.physa.2010.08.052

Balahur, A., & Turchi, M. (2014). Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech & Language, 28(1), 56-75. https://doi.org/10.1016/j.csl.2013.03.004

Banik, D., Ekbal, A., Bhattacharyya, P., & Bhattacharyya, S. (2019). Assembling translations from multi-engine machine translation outputs. Applied Soft Computing, 78, 230-239. https://doi.org/10.1016/j.asoc.2019.02.031

Choi, H., Cho, K., & Bengio, Y. (2017). Context-dependent word representation for neural machine translation. Computer Speech & Language, 45, 149-160. https://doi.org/10.1016/j.csl.2017.01.007

Choi, H., Cho, K., & Bengio, Y. (2018). Fine-grained attention mechanism for neural machine translation. Neurocomputing, 284, 171-176. https://doi.org/10.1016/j.neucom.2018.01.007

Costa-Jussa, M. R., & Fonollosa, J. A. (2015). Latest trends in hybrid machine translation and its applications. Computer Speech & Language, 32(1), 3-10. https://doi.org/10.1016/j.csl.2014.11.001

Costa-jussà, M. R., Allauzen, A., Barrault, L., Cho, K., & Schwenk, H. (2017). Introduction to the special issue on deep learning approaches for machine translation. Computer Speech & Language, 46, 367-373. https://doi.org/10.1016/j.csl.2017.03.001

Dew, K. N., Turner, A. M., Choi, Y. K., Bosold, A., & Kirchhoff, K. (2018). Development of machine translation technology for assisting health communication: A systematic review. Journal of biomedical informatics, 85, 56-67. https://doi.org/10.1016/j.jbi.2018.07.018

Dorr, B. J., Jordan, P. W., & Benoit, J. W. (1999). A survey of current paradigms in machine translation. Advances in computers, 49, 1-68. https://doi.org/10.1016/S0065-2458(08)60282-X

Firat, O., Cho, K., Sankaran, B., Vural, F. T. Y., & Bengio, Y. (2017). Multi-way, multilingual neural machine translation. Computer Speech & Language, 45, 236-252. https://doi.org/10.1016/j.csl.2016.10.006

Germann, U., Jahr, M., Knight, K., Marcu, D., & Yamada, K. (2004). Fast and optimal decoding for machine translation. Artificial Intelligence, 154(1-2), 127-143. https://doi.org/10.1016/j.artint.2003.06.001

Gulcehre, C., Firat, O., Xu, K., Cho, K., & Bengio, Y. (2017). On integrating a language model into neural machine translation. Computer Speech & Language, 45, 137-148. https://doi.org/10.1016/j.csl.2017.01.014

Harrat, S., Meftouh, K., & Smaili, K. (2019). Machine translation for Arabic dialects (survey). Information Processing & Management, 56(2), 262-273. https://doi.org/10.1016/j.ipm.2017.08.003

Hutchins, W. J. (1995). Machine translation: A brief history. In Concise history of the language sciences (pp. 431-445). Pergamon. https://doi.org/10.1016/B978-0-08-042580-1.50066-0

Madankar, M., Chandak, M. B., & Chavhan, N. (2016). Information retrieval system and machine translation: a review. Procedia Computer Science, 78, 845-850. https://doi.org/10.1016/j.procs.2016.02.071

Moussallem, D., Wauer, M., & Ngomo, A. C. N. (2018). Machine translation using semantic web technologies: A survey. Journal of Web Semantics, 51, 1-19. https://doi.org/10.1016/j.websem.2018.07.001

Navigli, R., & Ponzetto, S. P. (2012). BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial intelligence, 193, 217-250. https://doi.org/10.1016/j.artint.2012.07.001

Pecina, P., Dušek, O., Goeuriot, L., Hajič, J., Hlaváčová, J., Jones, G. J., ... & Urešová, Z. (2014). Adaptation of machine translation for multilingual information retrieval in the medical domain. Artificial intelligence in medicine, 61(3), 165-185. https://doi.org/10.1016/j.artmed.2014.01.004

Peris, Á., & Casacuberta, F. (2019). Online learning for effort reduction in interactive neural machine translation. Computer Speech & Language, 58, 98-126. https://doi.org/10.1016/j.csl.2019.04.001

Peris, Á., Domingo, M., & Casacuberta, F. (2017). Interactive neural machine translation. Computer Speech & Language, 45, 201-220. https://doi.org/10.1016/j.csl.2016.12.003

Petrucci, G., Rospocher, M., & Ghidini, C. (2018). Expressive ontology learning as neural machine translation. Journal of Web Semantics, 52, 66-82. https://doi.org/10.1016/j.websem.2018.10.002

Su, J., Zhang, X., Lin, Q., Qin, Y., Yao, J., & Liu, Y. (2019). Exploiting reverse target-side contexts for neural machine translation via asynchronous bidirectional decoding. Artificial Intelligence, 277, 103168. https://doi.org/10.1016/j.artint.2019.103168

Tan, Z., Su, J., Wang, B., Chen, Y., & Shi, X. (2018). Lattice-to-sequence attentional Neural Machine Translation models. Neurocomputing, 284, 138-147. https://doi.org/10.1016/j.neucom.2018.01.010

Wang, D. (2009). Chinese to English automatic patent machine translation at SIPO. World Patent Information, 31(2), 137-139. https://doi.org/10.1016/j.wpi.2008.10.003

Wołk, K., & Marasek, K. (2015). Neural-based machine translation for medical text domain. based on european medicines agency leaflet texts. Procedia Computer Science, 64, 2-9. https://doi.org/10.1016/j.procs.2015.08.456

Xiao, T., Zhu, J., & Liu, T. (2013). Bagging and boosting statistical machine translation systems. Artificial Intelligence, 195, 496-527. https://doi.org/10.1016/j.artint.2012.11.005

Xiao, Y., Keung, J., Bennin, K. E., & Mi, Q. (2018). Machine translation-based bug localization technique for bridging lexical gap. Information and Software Technology, 99, 58-61. https://doi.org/10.1016/j.infsof.2018.03.003

Yang, B., Wong, D. F., Chao, L. S., & Zhang, M. (2020). Improving tree-based neural machine translation with dynamic lexicalized dependency encoding. Knowledge-Based Systems, 188, 105042. https://doi.org/10.1016/j.knosys.2019.105042

Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of Technology Acceptance Model. Computers & Education, 133, 116-126. https://doi.org/10.1016/j.compedu.2019.01.015

Yang, Z., Chen, W., Wang, F., & Xu, B. (2018). Generative adversarial training for neural machine translation. Neurocomputing, 321, 146-155. https://doi.org/10.1016/j.neucom.2018.09.006