Hybrid Translation with Classification: Revisiting Rule-Based and Neural Machine Translation

Statistical Machine Translation with Rule based Machine Translation

We have evaluated the two-stage machine translation (MT) system. The first stage is a state-of-the-art trial rule-based machine translation system. The second stage is a normal statistical machine translation system. For Japanese-English machine translation, first, we used a Japanese-English rule-based MT, and we obtained "ENGLISH" sentences from Japanese sentences. Second, we used a standard s...

A Hybrid Rule and Example-based Method for Machine Translation

This paper introduces a new examplebased method of machine translation in which the examples need not be direct translations. The system will weed out strange examples during translation, allowing the use of currently available sentence aligned corpora as data. Rulebased modules are used where appropriate. A prototype Japanese-to-English system has been implemented that allows multiple users to...

Statistical Machine Translation Adding Rule-based 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...

Tree-based Hybrid Machine Translation

I present an automatic post-editing approach that combines translation systems which produce syntactic trees as output. The nodes in the generation tree and targetside SCFG tree are aligned and form the basis for computing structural similarity. Structural similarity computation aligns subtrees and based on this alignment, subtrees are substituted to create more accurate translations. Two diffe...