Improving Rule-Based Machine Translation by Using Statistical Syntactic Rules

نویسندگان
Hakimeh Fadaei
Heshaam Faili
Farnaz Ghasemi
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
چکیده
 Rule-based machine translation uses a set of linguistic rules in the process of translation. The results of these systems are usually better than the results of statistical models from grammatical and word order perspective, But it has been shown that statistical models are more powerful in selecting proper words and generating more fluent translations. In this paper our goal is to improve the word choice in rule-based machine translation. This is done by a set of lexical syntactic rules based on Tree Adjoining Grammar. These probabilistic rules are statistically extracted from a large parallel corpus. In the proposed system, the input sentence is first reordered by a rule-based system, and them the decoding is carried out monotonically by using dynamic programming. In this system the best translation is chosen based on the extracted rules and the language model score. The experiments on EnglishPersian translation showed that the proposed method resulted in an improvement of 1.3 in BLEU score in comparison to our baseline rule-based method. 

کلیدواژه‌ها

علوم رایانش و فناوری اطلاعات
دوره 14، شماره 2
پاییز و زمستان
پاییز 1395