Lexical semantics, Basque and Spanish in QTLeap:Quality Translation by Deep Language Engineering Approaches

QTLeap - Traducción de calidadmediante tratamientos profundos de ingenieŕıa lingǘıstica

Eneko Agirre, Iñaki Alegria, Nora Aranberri, Mikel Artetxe,Ander Barrena, António Branco (**), Arantza Dı́az de Ilarraza,

Koldo Gojenola, Gorka Labaka, Arantxa Otegi, and Kepa SarasolaIxa Taldea. Universidad del Pais Vasco /Euskal Herriko Unibertsitatea (UPV/EHU)

Manuel Lardizabal 1, -20018 Donostia(**) Universidade de Lisboa, Departamento de Informática,Faculdade de Ciências

e.agrirre@ehu.eus

Resumen: El objetivo de este proyecto europeo FP7 es contribuir a la mejora en lacalidad de la traducción automática mediante el uso de semántica, análisis sintáticoprofundo y el uso de datos abiertos entrelazados.Palabras clave: Traducción automática, Análisi profundo, Semántica, LOD

Abstract: The goal of this FP7 European project is to contribute for the advance-ment of quality machine translation by pursuing an approach that further relies onsemantics, deep parsing and linked open data.Keywords: Machine Translation, Deep language Engineering, Semantics, LOD ...

1 Summary

QTLeap project (Quality Translation byDeep Language Engineering Approaches)is a collaborative project funded by theEuropean Commission (FP7-ICT-2013.4.1-610516) that aims to produce high-qualityoutbound Machine Translation (MT) us-ing deep language engineering approaches toachieve higher quality translations (Brancoand Osenova, 2014). The approach is basedon deep processing and a transfer based ar-chitecture able to create hybrid systems. IXATaldea is the partner responsible of the devel-opments for semantics, Basque and Spanishin this project that is run by an Europeanconsortium with other seven partners: Bul-garian Academy of Sciences, Charles Univer-sity in Prague, German Research Center forArtificial Intelligence, Higher Functions Lda.,Humboldt University in Berlin, University ofthe Basque Country, University of Gronin-gen and University of Lisbon. The projectstarted in November 1st, 2013, and has a du-ration of 36 months.

The incremental advancement of researchon Machine Translation has been obtainedby encompassing increasingly sophisticatedstatistical approaches and fine grained lin-

guistic features that add to the surface levelalignment on which these approaches are ul-timately anchored. The goal of this projectis to contribute for the advancement of qual-ity MT by pursuing an approach that fur-ther relies on semantics and opens the wayto higher quality translation. We build onthe complementarity of the two pillars of lan-guage technology, symbolic and probabilistic,and seek to advance their hybridization. Weexplore combinations of them that amplifytheir strengths and mitigate their drawbacks,along the development of three MT pilot sys-tems that progressively seek to integrate deeplanguage engineering approaches.

The construction of deep treebanks hasprogressed to be delivering now the firstsignificant Parallel DeepBanks, where pairsof synonymous sentences from different lan-guages are annotated with their fully-fledgedgrammatical representations, up to the levelof their semantic representation.

The construction of Linked Open Dataand other semantic resources, in turn, hasprogressed now to support impactful applica-tion of lexical semantic processing that han-dles and resolves referential and conceptualambiguity.

Procesamiento del Lenguaje Natural, Revista nº 55, septiembre de 2015, pp 169-172 recibido 24-03-2015 revisado 27-04-2015 aceptado 15-05-2015

ISSN 1135-5948 © 2015 Sociedad Española para el Procesamiento del Lenguaje Natural

These cutting edge advances permit forthe cross-lingual alignment supporting trans-lation to be established at the level of deepersemantic representation. The deeper thelevel the less language-specific differences re-main among source and target sentences andnew chances of success become available forthe statistically based transduction.

English is the common language for theMT systems to be built in the project, beingas target or source for each one of the other7 languages in the project: Basque, Bulgar-ian, Czech, Dutch, German, Portuguese andSpanish.

2 Architecture:TectoMT

All the partners use a MT transfer-based ar-chitecture, being TectoMT the architectureused for almost all the language pairs (Czech,Spanish, Portuguese, Basque, and Bulgar-ian). TectoMT is a highly modular, struc-tural MT system implemented within theTreex NLP framework that allows for fastand efficient development of machine trans-lation by exploiting a wide range of softwaremodules already integrated in TectoMT, suchas tools for sentence segmentation, tokeniza-tion, morphological analysis, POS tagging,shallow and deep syn- tax parsing, named en-tity recognition, anaphora resolution, tree-to-tree translation, natural language generation,word-level alignment of parallel corpora, andtransfer based on deep syntactic (tectogram-matical) layer (Zeman et al., 2014). The tec-togrammatical layer is based on the PragueDependency Treebank. Figure 1 shows thearchitecture of the TectoMT system.

3 Deep Language Analysis forSpanish and Basque

Deep Language Analysis for Spanish andBasque will be implemented in our groupvia Ixa-pipes (Agerri, Bermudez, and Rigau,2014). IXA-pipes is a modular set of Nat-ural Language Processing tools (or pipes)which provide easy access to NLP technol-ogy for several languages. It offers robustand efficient linguistic annotation to both re-searchers and non-NLP experts with the aimof lowering the barriers of using NLP technol-ogy either for research purposes or for smallindustrial developers and SMEs. The ixa-pipes tools can be used or exploit its mod-ularity to pick and change different com-ponents (Morpholgy, POS tagger, Chunker,

Coreference, Named Entities Recognizer...).The tools are developed by the IXA NLPGroup of the University of the Basque Coun-try.

4 Semantic ways for MTimproving

The overall goal of the work package on lexi-cal semantics lead by Ixa Taldea is to enhanceMT with advanced crosslingual methods forthe resolution of referential and lexical ambi-guity by pursuing the following objectives:

• to provide for the assembling and cura-tion of the data sets and processing toolsavailable to support the resolution of ref-erential and lexical ambiguity,

• to leverage the resolution of referentialand lexical ambiguity by means of ad-vanced crosslingual named entity andword sense resolution methods,

• to proceed with the intrinsic evaluationof the solutions found in the previoustask,

• to contribute for high quality machinetranslation by using semantic linkingand resolving.

5 Real user scenario

QTLeap project successfully achieved thefirst year milestone last November relatedwith the identification of a real user scenariothat will show the suitability to use machinetranslation (‘Leveraging practical multilin-gual helpdesk services with machine trans-lation’). Namely in an IT helpdesk serviceprovided by HF, Higher Functions - Intelli-gent Information Systems Ltd, a PortugueseSME, which is a partner of the consortium.

With this service, if a user of an IT deviceor service needs to solve a problem, he/shecan ask a question for help through a chatchannel. If there is already a similar questionin the database, the associated response isimmediately delivered to the user. This pro-cess helps to minimize the human operation,which becomes needed only in those caseswhen there is no similar question-answer pairalready available in the database. The ap-plication of the machine translation systemextends this support service by allowing theuse of the seven languages in the project toask a question to the helpdesk service. The

Eneko Agirre, Iñaki Alegria, Nora Aranberri, Mikel Artetxe, Ander Barrena, António Branco, Arantza Díaz de Ilarraza, Koldo Gojenola, Gorka Labaka, Arantxa Otegi, Kepa Sarasola

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Figure 1: TectoMT architecture (Popel, 2014)

users can ask a question in their own lan-guages, which is translated into the languageof the questions and answers that are storedin the database. With the retrieved answeralso translated back to the language of theoriginal question, the helpdesk thus returnsthe response in the language of the user. Ex-amples of the questions the user could ask arethe following:

• Tengo un iPad. ¿Cómo abro una nuevapestana en Safari?

• ¿Cómo se qué versión de Photoshoptengo?

• En YouTube, ¿cómo puedo buscarv́ıdeos en HD?

• ¿Qué es una IP estática?• He instalado Linux. ¿Cómo puedo listar

los archivos en una carpeta?

• Ich habe ein iPad. Wie kann ich in Safarieinen neuen Tab öffnen?

• Wie kann ich auf YouTube nach Videosin HD suchen?

• Wie kann ich erfahren, welche Photo-shop Version ich habe?

• Was ist ein statisches IP?• Ich habe Linux installiert. Wie kann ich

die Liste der Dateien in einem Ordnererstellen?

• iPad bat dut. Nola ireki dezaket Safarifitxa berri batean?

• Nola jakin dezaket Photoshop-en zeinbertsio dudan?

• YouTube-n, nola bila ditzaket HDbideoak?

• Zer da IP estatiko bat?• Linux instalatu dut. Nola zerrenda

ditzaket fitxategiak karpeta batean?

Figure 2 shows an example of the workdone by the initial translators for Spanish.Although the answer given to the user is notbeing to be perfect, it uses to be suitableenough to resolve the question. The evalu-ation of this helpdesk service expanded with

Lexical semantics, Basque and Spanish in QTLeap: Quality Translation by Deep Language Engineering Approaches

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Figure 2: Example of use of PCMEDIC, thereal user scenario

the machine translation functionality showedthat even with a low-quality machine transla-tion system, it was already possible to achievea very significant reduction of human opera-tion of about 60% on average for each newlanguage to be covered by the service. Thistechnical advance leverages a great deal of ad-vantages for this kind of business with regardto its extension to the single digital market,as well for its improvement in terms of pro-ductivity and resources optimization, withthe consequent effective reduction of costs.

There is a common path of progressionfor each pair XEN, ensuring compara-bility of the research exercise: every pair isdeveloped along pilots 0 to 3 reinforced bycomplementary strengths and backgrounds ofthe different partners with their systems, re-sources and technology.

6 Advisory Board

The direction of the project is informed bythe advice on strategic issues from the Ad-visory Board of Potential Users. This Ad-visory Board includes industrial participantsthat are ready to contribute with their ad-vice on the strategic course of the projectactivities, and are interested in the innova-tion potential of the results targeted at bythe project and will be in the first row of thepotential users that will take the lead to ex-

ploit their business potential. The membersof this board are:

• CA Technologies Development SpainS.A (Spain)

• Eleka Ingeniaritza Linguistikoa SL(Basque Country, Spain)

• OMQ GmBH (Germany)• Lingea s.r.o. (Czech Republic)• Seznam.cz, a.s. (Czech Republic)• Higher Functions, Lda (Portugal, also

partner)

Information on QTLeap project andcontact details:Website: http://qtleap.eu/Facebook: https://www.facebook.com/qtleapTwitter: https://twitter.com/QTLeap

7 Aknowledgements

The research leading to these results wascarried out as part of the QtLeap projectfunded by European Community (FP7-ICT-2013.4.1-610516)

References

Agerri, R., J. Bermudez, and G. Rigau.2014. IXA pipeline: Efficient and Readyto Use Multilingual NLP tools. 9th Lan-guage Resources and Evaluation Con-ference (LREC2014), Reykjavik, Iceland.pages 26–36.

Branco, A. and P. Osenova. 2014. QTLeap- Quality Translation with Deep Lan-guage Engineering Approaches. Poster atEAMT2014, Dubrovnik.

Popel, M. 2014. MT Pilot 1: Entry-level Deep MT. Internal presentation inQTLeap project Meeting. Lisbon.

Zeman, D., O. Dušek, D. Mareček,M. Popel, L. Ramasamy, J. Štěpánek,Z. Žabokrtský, and J. Hajič. 2014.Hamledt: Harmonized multi-language de-pendency treebank. Language Resourcesand Evaluation, 48(4):601–637.

Eneko Agirre, Iñaki Alegria, Nora Aranberri, Mikel Artetxe, Ander Barrena, António Branco, Arantza Díaz de Ilarraza, Koldo Gojenola, Gorka Labaka, Arantxa Otegi, Kepa Sarasola

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