Statistical Machine Translation (SMT) is a well-known and well established datadriven approach used for language translation. The focus of this work is to develop a statistical machine translation system for Sri Lankan languages, Sinhala and Tamil language pair. This paper presents a systematic investigation of how SinhalaTamil SMT performance varies with the amount of parallel training data us...

This paper presents the overview of statistical machine translation systems that BJTU-NLP developed for the NTCIR-9 Patent Machine Translation Task (NTCIR-9 PatentMT). We compared the performance between phrase-based translation model and factored translation model in our Patent SMT of Chinese to English and English to Japanese. Factored translation model was proposed as an extended phrase-base...

This work discusses translation results for the four Euparl data sets which were made available for the shared task “Exploiting Parallel Texts for Statistical Machine Translation”. All results presented were generated by using a statistical machine translation system which implements a log-linear combination of feature functions along with a bilingual n-gram translation model.

Focus of this paper is the system for statistical machine translation developed at ITC-irst. It has been employed in the evaluation campaign of the International Workshop on Spoken Language Translation 2004 in all the three data set conditions of the Chinese-English track. Both the statistical model underlying the system and the system architecture are presented. Moreover, details are given on ...

German verbal inflection is frequently wrong in standard statistical machine translation approaches. German verbs agree with subjects in person and number, and they bear information about mood and tense. For subject–verb agreement, we parse German MT output to identify subject–verb pairs and ensure that the verb agrees with the subject. We show that this approach improves subject-verb agreement...

In this paper, we present a new hybridisation approach consisting of enriching the phrase table of a phrase-based statistical machine translation system with bilingual phrase pairs matching structural transfer rules and dictionary entries from a shallowtransfer rule-based machine translation system. We have tested this approach on different small parallel corpora scenarios, where pure statistic...

Conventional statistical machine translation (SMT) approaches might not be able to find a good translation due to problems in its statistical models (due to data sparseness during the estimation of the model parameters) as well as search errors during the decoding process. This paper1 presents an example-based rescoring method that validates SMT translation candidates and judges whether the sel...

In this paper we present a novel way of integrating Translation Memory into an Example-based Machine Translation System (EBMT) to deal with the issue of low resources. We have used a dialogue of 380 sentences as the example-base for our system. The translation units in the Translation Memories are automatically extracted based on the aligned phrases (words) of a statistical machine translation ...

In this paper we shall compare two approaches to machine translation: the Syntax Augmented Machine Translation system (SAMT), which is a syntaxdriven translation system, underlain by phrase-based model, and the n-gram-based Statistical Machine Translation (SMT), in which a translation process is based on statistical modeling of the bilingual context. We provide a step-by-step comparison of the ...