In this paper, we give a description of the machine translation system developed at DCU that was used for our first participation in the evaluation campaign of the International Workshop on Spoken Language Translation (2006). This system combines two types of approaches. First, we use an EBMT approach to collect aligned chunks based on two steps: deterministic chunking of both sides and chunk alignment. We use several chunking and alignment strategies. We also extract SMT-style aligned phrases, and the two types of resources are combined. We participated in the Open Data Track for the following translation directions: Arabic-English and Italian-English, for which we translated both the single-best ASR hypotheses and the text input. We report the results of the system for the provided evaluation sets.

Basque is both a minority and a highly inflected language with free order of sentence constituents. Machine Translation of Basque is thus both a real need and a test bed for MT techniques. In this paper, we present a modular Data-Driven MT system which includes different chunkers as well as chunk aligners which can deal with the free order of sentence constituents of Basque. We conducted Basque to English translation experiments, evaluated on a large corpus (270, 000 sentence pairs). The experimental results show that our system significantly outperforms state-of-the-art approaches according to several common automatic evaluation metrics. 1

A dissertation submitted in fulfilment of the requirements for the award of

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State-of-the-art statistical machine translation systems make use of a large translation table obtained after scoring a set of bilingual phrase pairs automatically extracted from a parallel corpus. The number of bilingual phrase pairs extracted from a pair of aligned sentences grows exponentially as the length of the sentences increases; therefore, the number of entries in the phrase table used to carry out the translation may become unmanageable, especially when online, ‘on demand ’ translation is required in real time. We describe the use of closed-class words to filter the set of bilingual phrase pairs extracted from the parallel corpus by taking into account the alignment information and the type of the words involved in the alignments. On four European language pairs, we show that our simple yet novel approach can filter the phrase table by up to a third yet still provide competitive results compared to the baseline. Furthermore, it provides a nice balance between the unfiltered approach and pruning using stop words, where the deterioration in translation quality is unacceptably high. 1

In this paper, we investigate the feasibility of combining two data-driven machine translation (MT) systems for the translation of sign languages (SLs). We take the MT systems of two prominent data-driven research groups, the MaTrEx system developed at DCU and the Statistical Machine Translation (SMT) system developed

Abstract This chapter has two main aims: (i) to present the state-of-the-art in Machine Translation (MT), namely Phrase-Based Statistical MT, together with the major competing paradigms used in MT research and development today; and (ii) to provide an overview of the MT research carried out by my team here at DCU, characterised here in terms of ‘hybrid MT’. In addition, we provide our views on the directions that MT research might take in the near future, and conclude the chapter with lists of further reading for the interested reader.

Abstract. We describe OpenMaTrEx, a free/open-source examplebased machine translation (EBMT) system based on the marker hypothesis, comprising a marker-driven chunker, a collection of chunk aligners, and two engines: one based on a simple proof-of-concept monotone EBMT recombinator and a Moses-based statistical decoder. OpenMa-TrEx is a free/open-source release of the basic components of MaTrEx, the Dublin City University machine translation system.

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Traslán makes full use of MT during our translation workflow, where the raw output from our Machine Translation (MT) system is passed onto human translators who perform post-editing (if necessary) to arrive at the final translation. Within Traslán we have found that using MT has enabled us to increase the speed, accuracy and consistency of translation- elements which allow us to process larger amounts of translation; with quicker turnaround times, which in turn has resulted in overall savings of approx. 20 % so far. One of the main challenges in using MT within a commercial setting is getting human translators to adopt and make full use of the technology. Within Traslán we overcome this obstacle by working closely and intensively with our translators, getting them involved directly in the development process. Doing so enables translators in turn to train new users of the system and to communicate effectively to other translators the benefits of integrating MT into the translation pipeline. 1