Thai Poetry Translation to English with Tuning Module
Sajjaporn Waijanya, Anirach Mingkhwan
Journal of Information & Systems Management, Vol. 5, No. 1, 2015.
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Abstract:
The complexities of poetry translation always challenges researchers in the field of NLP (National Language Processing) and MT (Machine Translation). This paper will focus on the Thai poetry type â€œKlonn-Padâ€ and aim to translate
into English keeping terms of prosody. The results of translation between MT Forward and Dictionary Base Forward will be
compared as well as between MT Backward and Dictionary Base Backward. BLEU (Bilingual Evaluation Understudy) metric will be used to compare between reference and candidates of English results. The abbreviations are as follows, T1 (G) translated by Google API, T1 (D) translated by Dictionary Base, T3 (G) translated by Google API with Tuning and T3 (D)
translated by Dictionary Base. The BLEU score of T1 (G) equaled 0.344, T1 (D) equaled 0.888, T3 (G) equaled 0.674 and T3 (D) equaled 0.889. The survey results regarding native Thai people opinions to this case study, sorted by highest score first was T3 (D), T1 (D), T3 (G), and T1 (G). Based on this study, it can be concluded that the machine translators are unable to
provide good enough translation for Thai poetry compared with the dictionary base. Thus, it is necessary and reasonable that Thai poetry needs to have a special dictionary with tuning algorithms to provide better results. Moreover, another reason is that poetry may not relate completely to syntax structures.