Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016; He et al. 2016). This is in contrast to conventional Statistical Machine Translation (SMT), which usually yields adequate but non-fluent translations. It is natural, th...

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...

This Paper aims to discuss how create the local language machine translation of Indonesia Language where reason selection was carried out as considering using translator for are still infrequently found mainly Dayak translator. Machine Translation on this research had used statistical approach resource data that taken originated from articles dayaknews.com pages with total parallel corpus appro...

In recent evaluations of machine translation systems, statistical systems have outperformed classical approaches based on interpretation, transfer, and generation. Nonetheless, the output of statistical systems often contains obvious grammatical errors. This can be attributed to the fact that the syntactic well-formedness is only influenced by local n-gram language models and simple alignment m...

In its early development, machine translation adopted rule-based approaches, which can include the use of language syntax. The late 1980s and early 1990s saw the inception of the statistical machine translation (SMT) approach, where translation models can be learned automatically from a parallel corpus rather than created manually by humans. Initial SMT models were word-based and phrase-based, ...