Enhancing English-Japanese translation using syntactic pattern recognition methods
In this paper, we present a novel approach to Machine Translation (MT) using syntactic Pattern Recognition (PR) methods. Our aim is to evaluate the possibility of using syntactic PR techniques in this mature field, and to identify any potential benefits that can be gleaned by such an approach. To make use of syntactic PR techniques, we propose a system that performs string-matching to pair English sentence structures to Japanese (The specific languages, namely English and Japanese, were chosen because their sentence structures are completely dissimilar. This, however, proves the point that such syntactic methods will be applicable for other pairs of languages too.) structures – as opposed to matching strings in and of themselves, and to thus facilitate translation between the languages. In order to process the sentence structures of either language as a string, we have created a representation that replaces the tokens of a sentence with their respective Part-of-Speech tags. Further, to perform the actual string-matching operation, we make use of the OptPR algorithm, a syntactic award-winning PR scheme that has been proven to achieve optimal accuracy, and that also attains the information theoretic bound. Through our experiments, we show that our implementation obtains superior results to that of a standard statistical MT system on our data set. Our results provide the additional guarantee of generating a known sentence structure in the target language. With further research, this system could be expanded to have a more complete coverage of the languages worked with. The incorporation of such PR techniques in MT, in general, and the OptPR algorithm, in particular, are both pioneering.
|Keywords||Machine translation (MT), Syntactic methods in MT|
|Series||Advances in Intelligent Systems and Computing|
McMahon, T. (Thomas), & Oommen, J. (2018). Enhancing English-Japanese translation using syntactic pattern recognition methods. In Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-59162-9_4