The paper defines corpus-based machine translation and its possible applications in machine translation. The study is based on a bilingual corpus of French and Arabic texts and translation unit alignment. The criteria used for alignment combine linguistic and statistical information. The study also suggests procedures to build a machine translation system based on parallel translated corpora.

The term ‘Machine Translation’ – commonly abbreviated MT – is historical and polysemous. Historically it points back to pre-computer times with occasional engineering attempts at developing mechanical translating devices (Hutchins 1986; 1995). An earlier synonym, ‘mechanical translation’, is not used any longer, but ‘machine translation’ remains in common use, even if ‘computer translation’ wou...

Machine Translation is area of research since six decades. It is gaining popularity since last decade due to better computational facilities available at personal computer systems. This paper presents different Machine Translation system where Sanskrit is involved as source, target or key support language. Researchers employ various techniques like Rule based, Corpus based, Direct for machine t...

We have developed a two-stage machine translation (MT) system. The first stage is a rule-based machine translation system. The second stage is a normal statistical machine translation system. For Chinese-English machine translation, first, we used a Chinese-English rule-based MT, and we obtained ”ENGLISH” sentences from Chinese sentences. Second, we used a standard statistical machine translati...

To aid research and development in machine translation, we have produced a test collection for Japanese/English machine translation and performed the Patent Translation Task at the Seventh NTCIR Workshop. To obtain a parallel corpus, we extracted patent documents for the same or related inventions published in Japan and the United States. Our test collection includes approximately 2 000 000 sen...

Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapter for the textbook Statistical Machine Translation. Used in the JHU Fall 2017...

Statistical MT is limited by reliance on large parallel corpora. We propose Lemmatic MT, a new paradigm that extendsMT to a far broader set of languages, but requires substantial manual encoding effort. We present PANLINGUAL TRANSLATOR, a prototype Lemmatic MT system with high translation adequacy on 59% to 99% of sentences (average 84%) on a sample of 6 language pairs that Google Translate (GT...

Transfer-Driven Machine Translation (TDMT) [1, 2] is a translation technique developed as a research project at ATR Interpreting Telecommunications Research Laboratories. In TDMT, translation is performed mainly by a transfer module which applies transfer knowledge to an input sentence. Other modules, such as lexical processing, analysis, contextual processing and generation, cooperate with the...

We describe our submissions to the WMT11 shared MT evaluation task: MTeRater and MTeRater-Plus. Both are machine-learned metrics that use features from e-rater R ©, an automated essay scoring engine designed to assess writing proficiency. Despite using only features from e-rater and without comparing to translations, MTeRater achieves a sentencelevel correlation with human rankings equivalent t...

This paper describes an approach to Machine Translation that places linguistic information at its foundation. The difficulty of translation from English to Japanese is illustrated with data that shows the influence of various linguistic contextual factors. Next, a method for natural language transfer is presented that integrates translation examples (represented as typed feature structures with...