OPEN ACCESS
Machine translation turns out to be an inherently complex process requiring serious attention to morphological, syntactic and semantic complexity within both the source and the target languages. Most of the existing approaches to machine translation (MT) circumvent the complexity with the assumption that morphological, syntactic and semantic analysis can be done independently and sequentially. This has resulted in depriving us of the opportunity to use the language complexity to generate high-quality translations. In view of this, research has been conducted to develop a multi-agent systems solution for MT that uses the language complexity as an opportunity for generating a more realistic translation from English to Sinhala. This multi-agent solution primarily comprises a six-agent swarm to deliberate on morphological, syntactic and semantic concerns of the source and the target languages without being constrained to operate in a sequential manner. These agents use the ontology of corpora and dictionary of two languages. This approach is inspired by the fact that people understand a sentence by incrementally reading through words while simultaneously considering the syntax and semantics. As such, when the system progresses in identification of words one by one, both syntactical and semantic concerns are entertained up to the current point of reading. As a result, initially decided words may be changed due to the present concern of morphology, syntax and semantics. A translation system has been implemented on the multi-agent system development framework named MaSMT. Experiments show that the multi-agent solution for MT gives promising results for translating sentences of an average length and further research has been carried out to accommodate translation of long sentences.
machine translation, multi-agent systems, Sinhala language.
[1] Hettige, B. & Karunananda A.S., Existing Systems and Approaches for Machine Translation: A Review, Proc. of the 8th Annual sessions on Sri Lanka Association for Artificial Intelligence (SLAAI), pp. 34–40, 2011.
[2] Google Translate Blog, googletranslate.blogspot.com
[3] Moses, www.statmt.org/moses/
[4] SYSTRAN, www.systransoft.com
[5] Forcada M.L., Tyers F.M. & Ramírez-Sánchez G., The Apertium machine translation platform: five years on, Proc. of 1st Int. Workshop on Free/Open-Source Rule-Based Machine Translation, pp. 3–10, 2009.
[6] Wiechetek L., Rule-based MT approaches such as Apertium and GramTrans, Online. uit.no/ Content/84556/mt.pdf
[7] Chaudhury S., Rao A. & Sharma D.M., Anusaaraka: An expert system based machine translation system, Proc. of 2010 Int. Conf. on Natural Language Processing and Knowledge Engineering (NLP-KE), pp. 1–6, 2010.
[8] Hettige B. & Karunananda A.S., A Computational grammar of Sinhala for English-Sinhala machine translation, Proc. of 2011 Int. Conf. on Advances in ICT for Emerging Regions (ICTer), pp. 26–31, 2011.
[9] Prasad T.V. & Muthukumaran G.M., Telugu to English Translation using Direct Machine Translation Approach., International Journal of Science and Engineering Investigations., 2(12), pp. 25–32, 2013.
[10] Okumura A. & Hovy E., Building Japanese-English dictionary based on ontology for machine translation, Proc. of workshop on Human Language Technology, pp. 141–146, 1994.
[11] Mandal D., Dandapat S., Gupta M., Banerjee P., & Sarkar S., Bengali and Hindi to English Cross-language Text Retrieval under Limited Resources, in Working Notes for the CLEF 2007 Workshop, 2007.
[12] Koehn P. & Hoang H., Birch A., Chris C., Federico M., Nicola B., Brooke C., Wade S., Christine M., Richard Z., Chris D., Ondrej B., Alexandra C., & Evan, Moses: Open Source Toolkit for Statistical Machine Translation, presented at the Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 2007.
[13] Nakazawa T., Yu K., Kawahara D., & Kurohashi S., Example-based machine translation based on deeper NLP, in IWSLT, pp. 64–70, 2006.
[14] Silva A.M. & Weerasinghe R., Example Based Machine Translation for English-Sinhala Translations, Proc. of 9th Int. IT Conference (IITC 2008), Colombo, Sri Lanka, pp. 27–28, 2008.
[15] Nithya B. & Joseph S., A Hybrid Approach to English to Malayalam Machine Translation., International Journal of Computer Applications 8(1), pp. 11–15, 2013.
[16] Liu Q. & Yu S., TransEasy: a Chinese-English machine translation system based on hybrid approach, in Machine Translation and the Information Soup, Springer, pp. 514–517, 1998.
[17] Google Translate, Wikipedia, the free encyclopaedia.
[18] Hettige B. & Karunananda A.S., A Morphological Analyser to Enable English to Sinhala Machine Translation, Proc. of Int. Conf. on Information and Automation, ICIA 2006, pp. 21–26, 2006.
[19] Hettige B. & Karunananda A.S., A Parser for Sinhala Language - First Step Towards English to Sinhala Machine Translation, Proc. of 1st Int. Conf. on Industrial and Information Systems, pp. 583–587, 2006.
[20] Hettige & Karunananda A.S., Transliteration system for English to Sinhala machine translation, Proc. of Int. Conf. on Industrial and Information Systems, ICIIS 2007, pp. 209–214, 2007.
[21] Liyanapathirana J. & Weerasinghe R., English to Sinhala Machine Translation: Towards Better information access for Sri Lankans, in Conference on Human Language Technology for Development, Alexandria, Egypt, pp. 182–186, 2011.
[22] Wikipedia: Ambiguous words, Wikipedia, the free encyclopaedia.
[23] Center T., Ambiguity Reduction for Machine Translation: Human-Computer Collaboration, Online. http://homes.cs.washington.edu
[24] Gaule M. & Josan G.S., Machine Translation of Idioms from English to Hindi, International Journal of Computational Engineering Research (ijceronline.com), 2(6), pp. 50–54, 2012.
[25] Suryakanthi T., Prasad S., Prasad T.V., Translation of Pronominal Anaphora from English to Telugu Language, International Journal of Advanced Computer Science and Applications, (IJACSA), 4(4), pp. 75–79, 2013.
[26] Hettige B., Karunananda A.S. & Rzevski G., MaSMT: A Multi-agent System Development Framework for English-Sinhala Machine Translation, Int. J. Comput. Linguist. Nat. Lang. Process. IJCLNLP, 2(7), pp. 411–416, 2013.
[27] Hettige B., Karunananda A.S. & Rzevski G., Sinhala Ontology Generator for English to Sinhala Machine Translation, Proc. of KDU International Research Conference, Colombo, 2014.
[28] Hettige B., Karunananda A.S. & Rzevski G., Multi-agent System Technology for Morphological Analysis, Proc. of 9th Annual session on Sri Lanka Association for Artificial Intelligence (SLAAI), pp. 1–7, 2012.
[29] Rzevski G. & Skobelev P. Managing Complexity, WIT Press, 2014.