A Survey on Hybrid Machine Translation

A Hybrid Machine Translation System Based on a Monotone Decoder

In this paper, a hybrid Machine Translation (MT) system is proposed by combining the result of a rule-based machine translation (RBMT) system with a statistical approach. The RBMT uses a set of linguistic rules for translation, which leads to better translation results in terms of word ordering and syntactic structure. On the other hand, SMT works better in lexical choice. Therefore, in our sys...

Machine Translation : a Survey

This paper aims at providing the reader with a comprehensive survey of the human quest for devising software for the translation of natural languages. This human quest resulted in what is called 'machine translation' (MT). It has left its imprint on the evolution of linguistics and computer science in the latter half of the 20 century and has an ever increasing impact on the developments in the...

Machine Translation Evaluation: A Survey

This paper introduces the state-of-the-art MT evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. We classify the automatic evaluation methods into two categories, including lexical similarity and linguistic features application...

Hybrid Machine Translation

Machine Learning for Hybrid Machine Translation

We describe a substitution-based system for hybrid machine translation (MT) that has been extended with machine learning components controlling its phrase selection. The approach is based on a rule-based MT (RBMT) system which creates template translations. Based on the rule-based generation parse tree and target-to-target alignments, we identify the set of “interesting” translation candidates ...