* Corresponding Author
Hide
Show all
Apps-based Machine Translation on Smart Media Devices - A Review
https://doi.org/10.22146/ijccs.43066 Hary Gunarto(1*)
(1) Ritsumeikan APU University
(*) Corresponding Author
Abstract
Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.
Keywords
Full Text:
PDFReferences
[1] Fu, H., 2019, Mixed language queries in online searches, Aslib Journal of Information Management, Vol. 71 Issue: 1, pp. 72-89. Bradford, DOI:10.1108/AJIM-04-2018-0091
[2] Abdalsamad, K. & Hossein, A., 2019, Bibliometrics of sentiment analysis literature, Journal of Information Science, Amsterdam Vol. 45, Issue: 1, pp. 3- , DOI:10.1177/0165551518761013
[3] Gunarto, H., 2004, Building Dictionary as Basic Tool for Machine Translation in Natural Language Processing Applications, Journal of Ritsumeikan Studies in Language and Culture, VOL 15, No 3, February 2004, pp: 177-185.
[4] Wibawa, A.P., Nafalski, A., 2013, Indonesian-to-Javanese Machine Translation, International Journal of Innovation, Management and Technology, Vol. 4, No. 4, August 2013, pp: 451-454.
[5] Abdiansah, A., Azhari, A., and Sari, A.K., 2018, Survey on Answer Validation for Indonesian Question Answering System (IQAS), Int. Journal Intelligent Systems and Applications, VOL. 4, pp. 68 - 78, DOI: 10.5815/ijisa.2018.04.08
[6] Kaiping, A.G., Klamer, M., 2018, LexiRumah: An online lexical database of the Lesser Sunda Islands, Leiden University Centre for Linguistics, Netherland, accessed on 5 January 2019 from https://doi.org/10.1371/journal.pone.0205250
[7] Biçici, E., Specia, L., 2015, QuEst for High Quality Machine Translation, The Prague Bulletin of Mathematical Linguistics, NUMBER 103, April 2015, pp. 43–64, DOI:10.1515/pralin-2015-0003
[8] Arcan, M., Turchi, M., Tonelli, S., Buitelaar, P., 2017, Leveraging bilingual terminology to improve machine translation in a CAT environment, J. of Natural Language Engineering; Cambridge Sep 2017, Vol. 23, Issue: 5, pp. 763-788. DOI:10.1017/S1351324917000195
[9] Das, B., Majumder, M., Phadikar, S., 2018, A Novel System for Generating Simple Sentences from Complex and Compound Sentences, I.J. Modern Education and Computer Science, 2018: 1, pp. 57-64, DOI: 10.5815/ijmecs.2018.01.06
[10] Moorkens, J., Way, A., 2016, Comparing Translator Acceptability of TM and SMT Outputs, Baltic Journal of Modern Computing, Vol. 4, Issue: 2, pp. 141-151.
[11] Linell, P., 2013, Distributed Language Theory, with or without Dialogue, Journal of Language Sciences, Elsevier, Volume 40, November 2013, pp. 168–173.
[12] Taghbalout, I., Allah, F.A., Marraki, M.E., 2018, Towards UNL-based machine translation for Moroccan Amazigh language, International Journal of Computational Science and Engineering; Geneva Vol. 17, Issue: 1, pp. 43-54. DOI:10.1504/IJCSE.2018.094418
[13] Gunarto, H., 2018, Kanji Learning. http://www.gunarto.org/kanji.php. accessed date 22 Nov. 2018.
[14] Tu, Z., 2016, DHT-based collaborative web translation, University of Cincinnati, ProQuest Dissertations Publishing.
[15] Abriata, L.A., 2017, Web Apps Come of Age for Molecular Sciences, J. Informatics; Basel Vol. 4, Issue: 3, pp. 28- DOI:10.3390/informatics4030028
[16] Gao, L., Gao, D., Xiong, N., Lee, C., 2018, CoWebDraw: a real-time collaborative graphical editing system supporting multi-clients based on HTML5, J. Multimedia Tools and Applications; Dordrecht Vol. 77, Issue: 4, pp. 5067-5082. DOI:10.1007/s11042-017-5242-4
[17] Voutilainen, J.P., Mattila, A.L., Systä, K., Mikkonen, T., 2016, HTML5-based Mobile Agents for Web-of-Things, J. Informatica, suppl. Engineering and Applications; Ljubljana Vol. 40, Issue: 1, pp. 43-51.
[18] Mobinizad, M.M., 2018, The Use of Mobile Technology in Learning English Language, J. Theory and Practice in Language Studies; London Vol. 8, Issue: 11, pp. 1456-1468. DOI:10.17507/tpls.0811.10
[19] Dale, R., 2019, Law and Word Order: NLP in Legal Tech, J. Natural Language Engineering; Cambridge Vol. 25, Issue: 1, pp. 211-217. DOI:10.1017/S1351324918000475
[20] Oprea, S., Tudorica, B.G., Belciu, A., Botha, I., 2017, Internet of Things, Challenges for Demand Side Management, Informatica Economica; Bucharest Vol. 21, Iss. 4, pp. 59-72.
[21] Qin, C.X., Qu, D., Zhang, L.H., 2018, Towards end-to-end speech recognition with transfer learning, EURASIP Journal on Audio, Speech, and Music Processing, Vol.18, pp. 1-9, DOI: https://doi.org/10.1186/s13636-018-0141-9
[22] Suhr, A., Lewis, M., Yeh, J., Artzi, Y., 2018, Evaluating Visual Reasoning Through Grounded Language Understanding, AI Magazine; Canada Vol. 39, Iss. 2, pp. 45-52.
[23] Israni, S.T., Verghese, A., 2019, Humanizing Artificial Intelligence, JAMA: The Journal of
the American Medical Association; Chicago Vol. 321, Issue: 1, pp. 29-, DOI:10.1001/jama.2018.19398
DOI: https://doi.org/10.22146/ijccs.43066
Article Metrics
Abstract views : 13307
| 
views : 4093
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1