A prototype English-Turkish statistical machine translation system

Official URL: http://192.168.1.20/record=b1293684 (Table of Contents)

Translating one natural language (text or speech) to another natural language automatically is known as machine translation. Machine translation is one of the major, oldest and the most active areas in natural language processing. The last decade and a half have seen the rise of the use of statistical approaches to the problem of machine translation. Statistical approaches learn translation parameters automatically from alignment text instead of relying on writing rules which is labor intensive. Although there has been quite extensive work in this area for some language pairs, there has not been research for the Turkish - English language pair. In this thesis, we present the results of our investigation and development of a state-of-theart statistical machine translation prototype from English to Turkish. Developing an English to Turkish statistical machine translation prototype is an interesting problem from a number of perspectives. The most important challenge is that English and Turkish are typologically rather distant languages. While English has very limited morphology and rather fixed Subject-Verb-Object constituent order, Turkish is an agglutinative language with very flexible (but Subject-Object-Verb dominant) constituent order and a very rich and productive derivational and inflectional morphology with word structures that can correspond to complete phrases of several words in English when translated. Our research is focused on making scientific contributions to the state-of-the-art by taking into account certain morphological properties of Turkish (and possibly similar languages) that have not been addressed sufficiently in previous research for other languages. In this thesis; we investigate how different morpheme-level representations of morphology on both the English and the Turkish sides impact statistical translation results. We experiment with local word ordering on the English side to bring the word order of specific English prepositional phrases and auxiliary verb complexes, in line with the corresponding case marked noun forms and complex verb forms, on the Turkish side to help with word alignment. We augment the training data with sentences just with content words (noun, verb, adjective, adverb) obtained from the original training data and with highly-reliable phrase-pairs obtained iteratively from an earlier phrase alignment to alleviate the dearth of the parallel data available. We use word-based language model in the reranking of the n-best lists in addition to the morpheme-based language model used for decoding, so that we can incorporate both the local morphotactic constraints and local word ordering constraints. Lastly, we present a procedure for repairing the decoder output by correcting words with incorrect morphological structure and out-of-vocabulary with respect to the training data and language model to further improve the translations. We also include fine-grained evaluation results and some oracle scores with the BLEU+ tool which is an extension of the evaluation metric BLEU. After all research and development, we improve from 19.77 BLEU points for our word-based baseline model to 27.60 BLEU points for an improvement of 7.83 points or about 40% relative improvement.