We describe OpenMaTrEx, a free/open-source example based

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. OpenMaTrEx is a free/open-source release of the basic components of MaTrEx, the Dublin City University machine translation system

Dandapat, Sandipan

Forcada, Mikel

Groves, Declan

Penkale, Sergio

Tinsley, John

Way, Andy

English

We describe OpenMaTrEx, a free/open-source example based\ud
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. OpenMaTrEx is a free/open-source release of the basic components of MaTrEx, the Dublin City University machine translation system

Name not available

OpenMaTrEx: A Free/Open-SourceMarker-Driven Example-Based MachineTranslation SystemSandipan Dandapat, Mikel L. Forcada⋆, Declan Groves⋆⋆, Sergio Penkale, JohnTinsley, and Andy WayCentre for Next Generation Localisation, School of Computing,Dublin City University, Glasnevin, Dublin 9, Ireland{sdandapat,mforcada,dgroves,spenkale,jtinsley,away}@computing.dcu.ieAbstract. We describe OpenMaTrEx, a free/open-source example-based machine translation (EBMT) system based on the marker hypoth-esis, comprising a marker-driven chunker, a collection of chunk align-ers, and two engines: one based on a simple proof-of-concept monotoneEBMT 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.Keywords: example-based machine translation, corpus-based machinetranslation, free/open-source software1 IntroductionWe describe OpenMaTrEx, a free/open-source (FOS) example-based machinetranslation (EBMT) system based on the marker hypothesis [1]. It comprises amarker-driven chunker, a collection of chunk aligners, and two engines: one basedon the simple proof-of-concept monotone recombinator (previously released asMarclator1) and a Moses-based decoder [2]. OpenMaTrEx is a FOS version ofthe basic components of MaTrEx, the Dublin City University machine transla-tion (MT) system [3, 4]. Most of the code in OpenMaTrEx is written in Java,although there are many important tasks that are performed in a variety ofscripting languages. A preliminary version, 0.71, has been released for downloadfrom http://www.openmatrex.org on 2nd June 2010, under a FOS licence.2The architecture of OpenMaTrEx is the same as that of a baseline Ma-TrEx system [3, 4]; as MaTrEx, it can wrap around the Moses statistical MT⋆ Permanent address: Departament de Llenguatges i Sistemes Informa`tics, Universitatd’Alacant, E-03071 Alacant, Spain⋆⋆ Also: Trasla´n Teoranta, 31 Mill Grove, Killincarrig, Greystones, Co. Wicklow, Ireland1 http://www.openmatrex.org/marclator/2 GNU GPL version 3, http://www.gnu.org/licenses/gpl.htmlTo be presented at IceTAL (Reykjavík, 16-18 Aug. 2010)2 Dandapat, Forcada, Groves, Penkale, Tinsley and Waydecoder, using a hybrid translation table containing marker-based chunks as wellas statistically extracted phrase3 pairs.OpenMaTrEx has been released as a FOS package so that MaTrEx com-ponents which have successfully been used [5–7] may be combined with com-ponents from other FOS machine translation (FOSMT) toolkits such as Cunei4[8], Apertium5 [9], etc.6 Indeed, using components released in OpenMaTrEx,researchers have previously: used statistical models to rerank the results of re-combination [10]; used aligned, marker-based chunks in an alternative decoderwhich uses a memory-based classifier [11]; combined the marker-based chun-kers with rule-based components [12], and used the chunker to filter out Mosesphrases for linguistic motivations [13].The rest of the paper is organized as follows. Section 2 describes the prin-ciples of training and translation in OpenMaTrEx; section 3 describes theEBMT-specific components in OpenMaTrEx; section 4 describes its softwarerequirements and briefly explains how to install and run the available compo-nents. A sample experiment performed on a standard task with OpenMaTrExis described in section 5 and results are compared to those obtained with a astandard statistical machine translation (SMT) system. Concluding remarks aremade in section 6.2 OpenMaTrEx: Training and TranslationTraining with OpenMaTrEx may be performed in two different modes. InMaTrEx mode:1. Each example sentence in the sentence-aligned source text and its counter-part in the target training text are divided in subsentential segments using amarker-based chunker. Chunks may optionally be tagged according to theirinitial marker word (to further guide the alignment process).2. A complete Moses–GIZA++7 training run is performed up to Moses step5 (phrase extraction). Moses is used to learn a maximum-likelihood lexicaltranslation table and to extract phrase-pair tables.3. The subsentential chunks are aligned using one of the aligners provided(using, among other information, probabilities generated by GIZA++).4. Aligned chunk pairs from step 3 are merged with the phrase pairs generatedby Moses in step 2 (more details in section 3).5. From then on, training proceeds as a regular Moses job after Moses step 6.MERT [14] may be used on a development set for tuning.In Marclator mode (see below), the last two steps are not necessary and Mosesis only run up to step 4.3 In statistical MT, the term phrase is stretched to refer to any contiguous sequenceof words.4 http://www.cunei.org5 http://www.apertium.org6 For a longer list of FOSMT systems, visit http://fosmt.info7 http://www.fjoch.com/GIZA++.htmlOpenMaTrEx: Free/Open-Source EBMT System 3Translation may be performed, as training, in two ways:– Marclator mode uses a monotone (“na¨ıve”) decoder (released as part ofMarclator): each source sentence is run through the marker-based chunker;the most probable translations for each chunk are retrieved, along with theirweights; if no chunk translations are found, the decoder backs off to the mostlikely translations for words (as aligned by GIZA++) and concatenates themin the same order, and when no translation is found, leaves any unfoundsource words untranslated. This decoder has obvious limitations, but it isfast and likely to be of most use in the case of closely related language pairs.– MaTrEx mode, however, is the usual way to use OpenMaTrEx; that is,the Moses decoder is run on a merged phrase table, as in MaTrEx [3, 4].3 EBMT-Specific ComponentsChunker: The main chunker in OpenMaTrEx is based on the marker hy-pothesis [1] which states that the syntax of a language is marked at the surfacelevel by a set of marker (closed-category) words or morphemes. The chunker inOpenMaTrEx deals with left-marking languages: a chunk starts at a markerword, and must contain at least one non-marker word. Punctuation is also usedto delimit chunks. Version 0.71 provides marker files for Catalan, Czech, English,Portuguese, Spanish, Irish, French and Italian. Marker files specify one markerword or punctuation in each line: its surface form, its category and (optionally)its subcategory. A typical marker word file contains a few hundred entries.Chunk aligners: There are a number of different chunk aligners available inOpenMaTrEx. The default aligner aligns chunks using a regular Levenshteinedit distance with a combination of costs specified in a configuration file, op-tionally allowing jumps or block movements [3]. The default combination usestwo costs: a probability cost based on word translation probabilities as calculatedby using GIZA++ and Moses (see training step 2 in section 2), and a cognatecost based on a combination of the Levenshtein distance, the longest commonsubsequence ratio and the Dice coefficient. As in [3], equal weights are used asa default for all component costs specified.Translation table merging: To run the system in MaTrEx mode, marker-based chunk pairs are merged with phrase pairs from alternative resources (here,Moses phrases). Firstly, each chunk pair is assigned a word alignment based onthe refined GIZA++ alignments, for example “please show me ||| por favormue´streme ||| 0-0 0-1 1-2 2-2”. In cases where there is no word alignmentfor a particular chunk pair according to GIZA++, the chunk pair is discarded.Using these word alignments, we additionally extract a phrase orientation-basedlexicalised reordering model a` la Moses [15]. Finally, we may also limit themaximum length of chunks pairs that will be used. The resulting chunk pairsare in the same format as those phrase pairs extracted by Moses. The next stepis to combine the chunk pairs with Moses phrase pairs. In order to do this, the4 Dandapat, Forcada, Groves, Penkale, Tinsley and Waytwo sets of chunk/phrase pairs are merged into a single file. Moses training isthen carried out from step 6 (scoring) which calculates the required scores forall feature functions, including the reordering model, based on the combinedcounts. A binary feature distinguishing EBMT chunks from SMT chunks maybe added for subsequent MERT optimization as was done in [16].4 Technical DetailsRequired software: OpenMaTrEx requires the installation of the followingsoftware: GIZA++, Moses, IRSTLM [17], and a set of auxiliary scripts for corpuspreprocessing8 and evaluation (mteval).9 Refer to the INSTALL file that comeswith the distribution for details.Installing OpenMaTrEx itself: OpenMaTrEx may easily be built simplyby invoking ant or an equivalent tool on the build.xml provided. The resultingOpenMaTrEx.jar contains all the relevant classes, some of which will be invokedusing a shell, OpenMaTrEx (see below).Running: A shell (OpenMaTrEx) has options to initialise the training, devel-opment, and testing sets, to call the chunker and the aligner, to train a targetlanguage model with IRSTLM, to run GIZA++ and Moses training jobs, tomerge marker-based chunk pairs with Moses phrase pairs, to run MERT opti-mization jobs, and to execute the decoders. Future versions will contain higher-level ready-made options for the most common training and translation jobs.For detailed instructions on how to perform complete training and translationjobs in both MaTrEx and Marclator mode, see the README file. Test files willbe provided in the examples directory of the OpenMaTrEx package.5 A Sample ExperimentTo show how OpenMaTrEx can be used to improve baseline SMT results, wereport on a simple experiment using 200,000 randomly selected sentences fromthe Spanish–English Europarl corpus provided for the Third Workshop on SMT(WMT08): testing was performed on the 2,000-sentence test set provided byWMT08. The experimental conditions are the same as those reported in [16].Table 1 shows results for (i) a baseline Moses job, (ii) a job in which marker-basedchunk pairs were transformed into Moses translation table pairs as described insection 3 and simply appended to the Moses phrase pairs, and (iii) a third jobin which an extra feature (having the value 1 for marker-based chunk pairs and0 for Moses-extracted phrase pairs) is added to the usual five features in allphrase pairs before MERT tuning. The table shows BLEU and NIST scores aswell as the fraction of phrase pairs used during translation that were extracted8 http://homepages.inf.ed.ac.uk/jschroe1/how-to/scripts.tgz9 We currently use version 11b from ftp://jaguar.ncsl.nist.gov/mt/resources/ .OpenMaTrEx: Free/Open-Source EBMT System 5by the marker-based chunker and aligner. Clearly, using the feature-informedphrase table merging improves the BLEU (with 93% statistical significance [18])and NIST scores (76% confidence), while simple merging does not seem to help.These improvements correlate nicely with the number of marker-based chunksactually used during translation. It would be interesting to pursue a more de-tailed study of the actual differences in the translations produced when usingmore linguistically-motivated chunk pairs.System BLEU NIST EBMT pairsBaseline Moses 30.59% 7.5171 27.60%Simple merging 30.42% 7.5156 29.53%Feature-based merging 30.75% 7.5269 33.55%Table 1: A sample experiment using 200,000 randomly-selected sentences from theSpanish-English fraction of Europarl, as provided for the Third Workshop on SMT(WMT08). Testing was performed on the 2,000-sentence test set provided by WMT08.6 Concluding Remarks and Future WorkWe have presented OpenMaTrEx, a FOS EBMT system including a marker-driven chunker (with marker word files for a few languages), chunk aligners,a simple monotone recombinator, and a wrapper around Moses so that it canbe used as a decoder for a merged translation table containing Moses phrasesand marker-based chunk pairs. OpenMaTrEx releases the basic components ofMaTrEx, the Dublin City University machine translation system under a FOSlicense, to make them available to researchers and developers of MT systems.As for future work, version 1.0 will contain, among other improvements,a better set of marker files, improved installing and running procedures withextensive training and testing options, and improved documentation; furtherversions are expected to free/open-source additional MaTrEx components.Acknowledgements. The original MaTrEx code on which OpenMaTrExis based was developed among others by S. Armstrong, Y. Graham, N. Gough,D. Groves, H. Hassan, Y. Ma, B. Mellebeek, N. Stroppa, J. Tinsley, and A. Way.We specially thank Y. Graham and Y. Ma for their advice. P. Pecina helped withCzech markers and Jim O’Regan with Irish markers. M.L. Forcada’s sabbaticalstay at Dublin City University is supported by Science Foundation Ireland (SFI)through ETS Walton Award 07/W.1/I1802 and by the Universitat d’Alacant(Spain). Support from SFI through grant 07/CE/I1142 is acknowledged.References1. T. Green. The necessity of syntax markers. two experiments with artificial lan-guages. Journal of Verbal Learning and Behavior, 18:481–496, 1979.6 Dandapat, Forcada, Groves, Penkale, Tinsley and Way2. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi,B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, andE. Herbst. Moses: Open source toolkit for statistical machine translation. Ann.Meeting of the Association for Computational Linguistics (ACL), demonstrationsession, Prague, Czech Republic, June, pages 177–180, 2007.3. N. Stroppa and A. Way. MaTrEx: DCU machine translation system for IWSLT2006. In Proceedings of IWSLT 2006, pages 31–36, 2006.4. N. Stroppa, D. Groves, A. Way, and K. Sarasola. Example-based machine transla-tion of the Basque language. In Proc. of AMTA 2006, pages 232–241, Cambridge,MA, USA, 2006.5. D. Groves and A. Way. Hybrid example-based SMT: the best of both worlds?ACL-2005 Workshop on Building and Using Parallel Texts: Data-Driven MachineTranslation and Beyond, 100:183–190, 2005.6. H. Hassan, Y. Ma, A. Way, and I. Dublin. MaTrEx: the DCU machine translationsystem for IWSLT 2007. In Proc. of IWSLT 2007, pages 69–75, Trento, Italy, 2007.7. J. Tinsley, Y. Ma, S. Ozdowska, and A. Way. MaTrEx: the DCU MT system forWMT 2008. In Proc. of the Third Workshop on Statistical Machine Translation,pages 171–174, Waikiki, HI, 2008.8. A.B. Phillips and R.D. Brown. Cunei machine translation platform: System de-scription. In Proc. of the 3rd Workshop on Example-Based Machine Translation,pages 29–36, Dublin, Ireland, Nov. 2009.9. F.M. Tyers, M.L. Forcada, and G. Ramı´rez-Sa´nchez. The Apertium machine trans-lation platform: Five years on. In Proc. of the First Intl. Workshop on Free/Open-Source Rule-Based Machine Translation, pages 3–10, Alacant, Spain, Nov. 2009.10. D. Groves and A. Way. Hybridity in MT: Experiments on the Europarl corpus. InProc. of the 11th Ann. Conf. of the European Association for Machine Translation(EAMT-2006), pages 115–124, Oslo, Norway, 2006.11. A. van den Bosch, N. Stroppa, and A. Way. A memory-based classification ap-proach to marker-based EBMT. In Proc. of the METIS-II Workshop on NewApproaches to Machine Translation, pages 63–72, Leuven, Belgium, 2007.12. F. Sa´nchez-Mart´ınez, M.L. Forcada, and A. Way. Hybrid rule-based – example-based MT: Feeding Apertium with sub-sentential translation units. In Proc. ofthe 3rd Workshop on Example-Based Machine Translation, pages 11–18, Dublin,Ireland, Nov. 2009.13. Felipe Sa´nchez-Mart´ınez and Andy Way. Marker-based filtering of bilingual phrasepairs for SMT. In Proc. of EAMT-09, the 13th Ann. Meeting of the EuropeanAssociation for Machine Translation, pages 144–151, Barcelona, Spain, 2009.14. F.J. Och. Minimum error rate training in statistical machine translation. In Proc.41st Ann. Meeting of the Association for Computational Linguistics-Volume 1,pages 160–167, Sapporo, Japan, 2003.15. P. Koehn, A. Axelrod, A.B. Mayne, C. Callison-Burch, M. Osborne, and D. Talbot.Edinburgh system description for the 2005 IWSLT speech translation evaluation.In Proc. of IWSLT 2005, Pittsburgh, PA, 2005.16. A. Srivastava, S. Penkale, D. Groves, and J. Tinsley. Evaluating syntax-drivenapproaches to phrase extraction for MT. In Proc. of the 3rd Workshop on Example-Based Machine Translation, pages 19–28, Dublin, Ireland, November 2009.17. M. Federico and M. Cettolo. Efficient handling of n-gram language models for sta-tistical machine translation. In Proc. of the 2nd Workshop on Statistical MachineTranslation, pages 88–95, Prague, Czech Rep., 2007.18. P. Koehn. Statistical significance tests for machine translation evaluation. InProceedings of EMNLP, volume 4, pages 388–395, 2004.

OpenMaTrEx: a free/open-source marker-driven example-based machine translation system

DCU Online Research Access Service

A memory-based classiﬁcation approach to marker-based EBMT.

Cunei machine translation platform: System description.

Edinburgh system description for the 2005 IWSLT speech translation evaluation.

Eﬃcient handling of n-gram language models for statistical machine translation.

Evaluating syntax-driven approaches to phrase extraction for MT.

Example-based machine translation of the Basque language.

Hybrid example-based SMT: the best of both worlds?

Hybrid rule-based – examplebased MT: Feeding Apertium with sub-sentential translation units.

Hybridity in MT: Experiments on the Europarl corpus.

ırez-S´ anchez. The Apertium machine translation platform: Five years on.

Marker-based ﬁltering of bilingual phrase pairs for SMT.

MaTrEx: DCU machine translation system for IWSLT

MaTrEx: the DCU machine translation system for IWSLT

MaTrEx: the DCU MT system for WMT

Minimum error rate training in statistical machine translation.

Moses: Open source toolkit for statistical machine translation. Ann. Meeting of the Association for Computational Linguistics (ACL), demonstration session,

Statistical signiﬁcance tests for machine translation evaluation.

The necessity of syntax markers. two experiments with artiﬁcial languages.

http://doras.dcu.ie/15797/1/OpenMaTrEx_A_Free_Open-Source.pdf

OpenMaTrEx: a free/open-source marker-driven example-based machine translation system

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