MARíA ROSARIO BAUTISTA ZAMBRANADepartment of Translation and InterpretingUniversity of Malaga

Malaga, Spainmrbautista@uma.es

Natural Language Generatíon and Translatíon Technologíes1

1. Introduction

New technologies and the arrival of globalization have had such an impact on the field of translation, thatnowadays, in the current Information Society, it is unthinkable for a translator to work without making use of theso-called Translation Technologies (TT). These are software applications that automate translation projectmanagement, allow collaborative teamwork in virtual environments, optimize peiiormance, and guarantee qualityby the use of computer-assisted tools (CAT) and terminology databases (cf. Alcina, 2004; Langewis, 2004). Byextension, Translation Technologies can be defined as a subdiscipline of Language Technologies that bearsrelatio~ to Translation S~udies, Natura! Language Processing and Computational Linguistics and that covers,accordlng to the Translabon Technologles Watch , the followlng areas: creaban, management and exploitation of

corpora, translation memories, software localization, creation and management of terminology databases, pre-and post-editing techniques, controlled languages, standards used in the translation industry, and machinetranslation (MT). A more recent and innovative technology can be added to the aforementioned ones natural

language generation (NLG), also called text generation

Natural language generation consists in the production of natural language texts from an abstract semanticknowledge representation, called interlingua (cf. Scott and Evans, 1998; Jurafsky and Martin, 2000), and standsout as one of the most innovative fields within Translation Technologies Recently, John Hutchins, one of thegreat precursors of TT and MT, considered the incipient research on NLG to be one of the most promising fieldsin the area of TT and pointed out that it would be eventually integrated into MT systems:

Users will want seamless integration of information retrieval, extraction and summarization systems with translation.Research has begun in such areas as cross-lingual information retrieval, multilingual summarization, multilingual textgeneration from databases, and so forth and, before many years, there may well be systems available on the marketand the Internet. (Hutchins, 2005: 3)

The importance of Translation Technologies is demonstrated when we take into account the large number ofprojects related to language engineering and translation that have been carried out in Europe, North America and

Spain for years. Furthermore, text generation has been the object of numerous European research projects,financed by the several Framework Programmes of the European Community, especially the last ones: HumanLanguage Technologies (1998-2002), e-Content.(2001-2004), Information Society Technologies (1ST) -within the

Sixth Framework Programme (2002-2006)- and e-Contentplus (2005-2008).

In Spain, the University of Malaga has specifically a wide experience in multilingual text generation, on which ithas been working since 1999. The first project dealing with this subject, called A Textual Typologiser for theMachine- Translation of Legal Texts (Spanish/English/German/ltalian/Arabic) [Diseño de un tipologizador textualpara la traducción automática de textos jurídicos (español/inglés/alemán/italiano/árabe)] (Spanish Ministry ofEducation and Science, ref. no. PB98-1399), was focused on the elaboration of a corpus-based NLG system forthe automatic production of real estate purchase contracts (see Carpas Pastor, 2003a). The next project aboutNLG, currently going on, involves the study of corpus-based multilingual NLG for the production of tourismcontracts: TURICOR: A multilingual corpus of tourism contracts (German, Spanish, English, Italian) for automatictext generation and legal translation [TURICOR: compilación de un corpus de contratos turísticos (alemán,español, inglés, italiano) para la generación textual multilingüe y la traducción jurídica] (Spanish Ministry ofScience and Technology, ref. no. BBF2003-04616, 2003-2006). A new project will continue the research intoGLN: Multilingual electronic tourism contracts as intercultural mediation: legal, translation and terminologicalaspects [La contratación turística electrónica multilingüe como mediación intercultural: aspectos legales,traductológicos y terminológicos] (Junta de Andalucía, HUM-892).

2. What is naturallanguage generation?Many authors have proposed definitions of natural language generation, Some of the most rel!3vant are the onesformulated by Hovy (1996) and Reiter and Dale (2000: 1) The former states the following: "The area of studycalled natural language generation (NLG) investigates how computer programs can be made to produce high-quality natural language text from computer-internal representations of information", For Reiter and Dale (2000:1), NLG systems are computer software systems that start from some type of non-linguistic representation of

1 The research reponed in this paper has been carried out in the tramework ot R & D projects Ret. nos. BBF2003-04616

(Funding source: Spanish Ministry ot Science and Technology, National R&D Program, and European Unjan, EuropeanRegional Development Fund-ERDF. 2003-2006) and HUM-892 (Andalusian Ministry tor Education, Spain 2006-2009)2 The Translation Technologies Watch (Observatorio de Tecnologías de la Traducción) is a thematic network financed by the

Spanish Ministry ot Educatíon and Science in the field ot Translation and Intormation Technologies (ret.: TIC2002-11705-E).Website: http//wwwuemes/web/ott/ [Last visited: 15-8-2006]

377

information as input and rely on knowledge about language and the application domain to automatically producereports,documents, explanations and other kind of textsIn short, NLG consists in the production of natural language texts from an abstract semantic knowledgerepresentation, called interlingua. In this way, a NLG system takes into account abstract information (generallynon-linguistic, unequivocal and well-structured information), as well as data about the communicative situation, inorder to produce a text with a coherent structure and appropriate linguistic expressions. In the case of multilingualgeneration, the system is able to produce texts in several languages from the same abstract source, either in a

simultaneous or sequential way.As pointed out above, the input provided to a NLG system is generally of a non-linguistic nature (Dale, Di Eugenioand Scott, 1998: 347): thus, it can be symbolic (e g., taken from an expert system knowledge base) or numeric(e. g., taken from a database containing stock market prices) However, there are some systems, such asCourseViewGenerator (Barrutieta, 2001), which do rely on the use of linguistic input.

The applications of NLG are varied: SIGGEN3, the Special Interest Group in Natural Language Generation,mentions report generation, machine translation, and explanations for knowledge-based systems; Bateman andZock (2003: 286) add text summarization and multilingual and multimodal presentation of information. Aguayo elal. (2003: 23), for their part, state that NLG systems can be used for preparing weather reports, visualizing thecontent of a database, generating automati~ speech, etc.,. and highlight its applicati~n in th~ fi~ld of machinetranslation between natural languages and In the translatlon from a knowledge representatlon Into numerouslanguages. Likewise, Langkilde (2002: 1) states that NLG is a subtask of many applications:

Such applications include machine translation, human-computer dialogue, summarization, report creation, automatictechnical documentation, proof/decision explanation, customized instructions, item and event descriptions, questionanswering, tutorials, stories, and more.

As we can see, the link between NLG and machine translation is highlighted by many authors, as the technologyapplied in NLG is quite similar to the latest developments in machine translation: example-based machinetranslation (EBMT). However, there are significant differences between both systems, since a NLG system doesnot start from a source text to get to a target text, but produces original texts in one or several languages; aprevious analysis stage is not required either, beca use linguistic information and other specifications come fromstructured relational database systems (cf. Aguayo el al., 2003: 23).

NLG methods or processes can be classified according to their sophistication and expressive power EduardHovy (1996) distinguishes tour generation methods: canned text systems, template systems, phrase-based

systems, and feature-based systems.

Canned text systems, according to the author, are used in the majority of software and consist in the presentationof strings of words without any change (error messages, warnings, letters, etc.). The approach can be usedequally easily for single-sentence and for multi-sentence text generation, but it proves insufficient when we need

to adapt the text to different situations.

Template systems represent the next level of sophistication and are used to produce similar messages, in which afew open fields are filled in specified constrained ways The template approach is used mainly for multisentencegeneration, particularly in applications whose texts are fairly regular in structure such as form letters and somebusiness reports. Examples of this kind of systems are ANA, TEXT and TAILOR. Additionally, many other

software applications use this method, such as ELlZA.

In the third place, Hovy mentions phrase-based systems, which employ generalized templates, whether at thesentence level or at the discourse level. He explains that in such systems, a phrasal pattern is first selected tomatch the top level of the input (say, [SUBJECT VERB OBJECT]), -and then each part of the pattern is expanded

into a more specific phrasal pattern that matches some subportion of the input (say, [DETERMINERADJECTIVES HEAD-NOUN MODIFIERS]), and so on; the cascading process stops when every phrasal patternhas been replaced by one or more words. Phrase-based systems can be powerful and robust, but are very hardto build beyond a certain size; that is why this method is used mainly for single-sentence generation. A well-

known example is MUMBLE.

Finally, feature-based systems are among the most sophisticated generators. According to Hovy, in feature-basedsystems, each possible minimal alternative of expression is represented by a single feature; for example, asentence is either positive or negative, it is a question or an imperative or a statement, its tense is present or pastand so on. In this way, each sentence is specified by a unique set of features. The researcher continues toexplain that generation proceeds by the incremental collection of features appropriate for each portion of the input(either by the traversal of a feature selection network or by unification), until the sentence is fully determined. This

type of system has strengths and weaknesses: "Their strength lies in the simplicity of their conception: anydistinction in language can be added to the system as a feature. Their weakness lies in the difficulty ofmaintaining feature interrelationships and in the control of feature selection (the more features available, the morecomplex the input must be)" (Hovy, 1996). The most studied and used generators are PENMAN/KPML and FUF.Some outstanding ones are COMMUNAL, SUTRA, SEMTEX and paPEL.

3 We can find more information at the URL http://wwwsiggen.org/. SIGGEN is the most important interest group devoted to the

study of NLG. [Last visited: 1-9-2006]

378

3. Multilingual text generation

Multilingual text generation constitutes one of the most interesting applications of NLG, due to its capacity togenerate documents in severallanguages from a sol e source of information: the conceptual choices made by theuser. lis uses in the framework of the current globalization are numerous: for example, many multinationalcompanies and international organizations need the constant production of large quantities of documentation; forits part, in recent years the tourism sector has seen a considerable increase in the number of bookings made onthe Internet, which implies in many cases the need to create multilingual forms, general terms of business, andcontracts. On the other hand, we are not unaware of the possibilities of NLG in the field of translation, as the lattercan benefit from many of the former's multilingual applications: in this way, a generation system can serve as avaluable source of parallel texts for the translator, who can not only obtain documents in several languages, butalgo select different conceptual options in a language and obtain their equivalents in the rest of them. In this way,the documentation process would speed up. For example, Scott and Evans (1998) propase a technique calledSymbolic Authoring, which allows to manually specify the symbolic (conceptual) content of the document andthenobtain the linguistic realisation in a given language. Additional generators for other languages can be added, so,as the researchers explain, a single authoring process supports multilingual variants of a document directly: oneupdate to the document is reflected in alllanguages simultaneously. Additionally, each generator can be adaptedto its own language and cultural settings, choosing its own most appropriate realisation strategy independently of

the others.Many authors remark the great possibilities of multilingual generation. It is the case of Lavid (2005),Androutsopoulos, Kallonis and Karkaletsis (2005) and Hartley and Paris (1997), who have written papers in thisarea of research. Lavid (2005) has worked on the development of grammar resources for English-Spanishcontrastive generation. Androutsopoulos, Kallonis and Karkaletsis (2005) have created a generator to describeobjects from ontologies. On the other hand, Hartley and Paris (1997) created, together with other researchers, theDRAFpER system, which they conceive to be a help tool for writers and translators:

We argue Ihal (...) mullilingual generalion provides Ihe appropriale lechnology, shifling allenlion lo an even earlierslage in Ihe aulhoring process, Ihal of specifying Ihe semanlics of Ihe lexl lo be produced. We describe a prololypesyslem which exploils Ihis lechnology lo meel Ihe expressed needs of aulhors and Iranslalors by supporting Ihem inIhe drafling of mullilingual inslruclions. We suggesl Ihal, in Ihe fulure, a single plalform lo support mullilingualdocumenlalion should inlegrale Iranslalion-orienled lools and generalion-based lools lo be employed as approprialeby differenllypes of users (Iranslalors and aulhors) in differenl circumslances (Hartley and Paris, 1997: 109)

Multilingual generation algo relates intimately to automatic translation, since many MT systems have been createdfrom the concept of interlingua or underlying representation of NLG. Some examples of this are the papers of Dorr(1993) (who created the UNITRAN system), Wilcock (1993) and Lavoie et al. (2000). Also, as we pointed outbefore, the technology applied in NLG is quite similar to the latest developments of example-based MT systems,as both are based on .data that have been extracted from corpus to produce the final texto In this sense, it is quitecommon that the creation of a NLG system implies carrying out inter- and intralinguistic studies to determine thetextual features of the field in question (cf. Aguado and Bernardos, 2000; Reiter and Dale, 1997).

In general, the methods to perform text generation in two or more languages do not differ much from those usedfor monolingual generation, especially if the languages involved are somewhat related. The basic idea behind themajority of multilingual systems is to make good use of the same input abstract information, just to generate textsin different languages later on; in this sense, the most studied fields regarding multilingual generation arelexicalization and grammar production, since these fields are the most relevant in the process of choosing the

right linguistic expressions to be conveyed.

A final outstanding aspect with regard to multilingual generation is the importance and usefulness that ontologieshave for its development. An ontology, according to Lavid (2005: 178), is an inventory of concepts organizedunder an internal structuring principie; that is, it is a concept hierarchy of a certain field, where we can see the

relationships existing between the concepts and the properties that they have Their terminological applicationsare well-known and have meant a majar breakthrough in the field of Terminology: WordNet (Miller, 1990;Fellbaum, 1998) and MikroKosmos (Viegas et al., 1996) -the latter, used algo for machine translation- are goodexamples of that. Likewise, the field of transjation has benefited from the advances in the studies aboutontologies: these are precisely the agglutinating axis that allows many NLG systems to generate texts in severallanguages, as they are capable of representing the conceptual structure and the common macrostructures thatunderlie two or more languages in a specific domain. On the other hand, as Bernardos (2003: 232) points out, theuse of ontologies in NLG is more than justified, because they allow the reutilization of information resources, forboth multilingual generation and other related applications. Some examples of ontologies used for text generationare Upper Model (Bateman, 1997; Reiter and Dale, 2000) and SENSUS (Knight and Luk, 1994; Hovy, 1998)

4. A NLG system: TURIGEN

The research carried out in the TURICOR Project (Ref. no. BBF2003-04616), in whose framework this paper ispresented, has resulted in the creation of the TURIGEN system, a multilingual generator of tourism contracts,capable of generating documents in tour languages (Spanish, English, German and Italian) in a sequential way.The first version of the program has specifically focused on the production of law clauses for the generalconditions of the holiday package contract, in the tour languages mentioned.

The generation of the texts starts, in the first place, from textual prototypes of the package holiday contract, onefor each language. The prototypes consist, as Carpas Pastor (2003b: 51) explains, in texts prepared artificially in

379

electronic form, written according to the superstructure and other characteristic maGro. and microstructuralelements of the document in question. These prototypes have been obtained through the detailed study andanalysis of the linguistic, textual and discursive characteristics of the contracts hosted at the Turicor corpus.

Once the textual prototypes have been created, we proceed to build a suitable interlingua for the computationalprocessing of those data and the subsequent generation of the final text in each one of thelanguages involved:Spanish, English, German and Italiano This interlingua, acting as a master document, represents the underlyingconcepts (macrostructure) and the textual realizations that match with each one of the concepts of the contractThe interlingua is specifically represented by means of a grammar tagged in XML which is capable of reflectingthe superstructure, the macrostructure and the microstructure of a document (in our case, a contract): XGTLlNG.

After the textual prototypes have been introduced in the TURIGEN application by means of the tagged grammar(the interlingua), the user begins to playa very important role, as he decides, using the interface, which parts ofthe contract and which concepts are going to appear in the final text, which value the variables are going totake,which textual forms are going to be selected, etc. In the end we obtain a document (e g. a law clause aboutpackage holidays) in tour languages that complies with the laws of the respective countries.

5. Conclusion

Natural language generation can be highly useful in the framework of the current Information Society: it has notonly applications in the production of multilingual documentation and in the promotion of electronic contracts ayerthe Internet (making it possible, for example, to automatically generate customized forms and contracts), but, inan interesting and original way, it can constitute a tool for obtaining parallel texts, which are highly important fortranslators in their daily work.

As a demonstration of that, we have built a sequential multilingual generator for tourism contracts. Furthermore,as we have proved, it is perfectly feasible to formalize this type of texts and convert them in interlinguas capableof serving as a bridge between the conceptual and linguistic choices of the user and the final texto The grammar

XGTLlNG, tagged in XML, constitutes an ideal means for that, as it can be adapted to any text type and language,provided that we previously build textual prototypes suitable for thetext type in question.

6. Bibliography

Aguado, G. and Bernardos, S. 2000. «Método para la elaboración de un corpus para la GLN» ron line].Procesamiento del Lenguaje Natural. 26. URL: http://wwwsepln.org/revistaSEPLN/revista/26/aguado-de-cea.pdf [Last visited: 25-8-2006].

Aguayo Maldonado, A., Caro Herrero, J. L., Carpas Pastor, G., Gómez Gallego, 1. M. and Guevara Plaza, A. J.2003. «Traducción automática y generación textual: herramientas, grupos y proyectos de investigación».In G. Carpas Pastor (ed.). 2003a.

Alcina, A. 2004. I Curso de Postgrado en Tecnologías de la Traducción ron line] URL:http://www3.uji.es/-alcina/cursos/tecn-trad.htm [Last visited: 15-12-04].

Androutsopoulos, l., Kallonis, S. and Karkaletsis, V. 2005. «Exploiting OWL Ontologies in the MultilingualGeneration of Object Descriptions» ron line] In Proceedings of the 10th European Workshop on NaturalLanguage Generation (ENLG 2005), Aberdeen, United Kingdom. 150-155. URL:

http://www.aueb.gr/users/ion/docs/enlg2005_paper.pdf [Last visited: 19-8-2006].Barrutieta, G. 2001. Generador inteligente de documentos de formación ron line]. URL:

http://sapiens.ya.com/cyberformacion/Generador.pdf [Last visited: 26-8-2006].Bateman, J. 1997. «Enabling technology for multilingual natural language generation: the KPML development

environment». Journal of Natural Language Engineering. 3 (1): 15-55

Bateman, J. and Zock, M. 2003. «Natural Language Generation». In R. Mitkov (ed.) 2003.

Bernardos, S. 2003. Marco metodo/ógico para la construcción de sistemas de generación de lenguaje natural.Doctoral thesis, Facultad de Informática, Universidad Politécnica de Madrid (Spain).

Carpas Pastor, G. (ed.). 2003a. Recursos documentales y tecnológicos para la traducción del discurso jurídico(español, alemán, inglés, italiano, árabe). Granada: Comares.

Carpas Pastor, G. 2003b. «Diseño de un tipologizador para la traducción jurídica: del corpus al prototipo textual».In G. Carpas Pastor (ed.). 2003a.

Dale, R., Di Eugenio, B. and Scott, D. 1998. «Introduction to the Speciallssue on Natural Language Generation»ron line]. Computational Linguistics 24 (3). 345-353. URL: http://www.cs.mu.oz.au/acl/J/J98/J98-3001.pdf [Last visited: 16-8-2006].

Dorr, B. 1993. «Interlingual machine translation: a parameterized approach». Artificiallntelligence. 63.

Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. Cambridge, Mass.: MIT Press.Hartley, A. and París, C. 1997. «Multilingual Document Production From Support for Translating to Support for

Authoring». Machine Translation. 12. 109-129.

Hovy, E. 1996. «Overview (of Language Generation)). In: R. Cole, J. Mariani, H. Uszkoreit, A. Zaenen and V. Zue(eds.) Survey of the Sta te of the Art in Human Language Technology ron line] URL:http://cslu.cse.ogi.edu/HL Tsurvey/ch4node3.html#SECTION41 [Last visited: 15-8-2006].

380

y, E. 1998. «Combining and Standardizing Large-Scale, Practical OntologiesOther Uses». In Proceedings of the First /nternationa/ Conference o,Eva/uation (LREC) , Granada.

:hins, J. 2005. «Computer-based Translation in Europe and North ¡JAP/O 20th anniversary. Tokyo: Japan Patent

http://ourworldcompuserve.com/homepages/WJHutchins/JAPIO--,y, D. and Martin, J. H. 2000. Speech and Language Processinf

Processing, Computationa/ Linguistics, and Speech Recognitiol

>r Machine Translation andLanguage Resources and

merica, and its Future Prospects». InInformation Organization URL:

005.pdf [Last visited: 15-8-2006].

An introduction to Natural LanguageUpper Saddle River (USA): Prentice

rafs~

p,;e for Machine TranslationKnight, K. and Luk, S. 1994. «Building a Large-Scale Knowledge Eof the American Association of Artificiallntelligence AAAI-~

- s, C. 2004. «What is language technology?» ton line] AComputing & Technology. URL: https:I/216.18.156.115/nvisited: 15-8-2006].

angkilde, l. 2002. «An Empirical Verification of Coverage andGenerator» ton line]. In Proceedings of the International ¡http://faculty.cs.byu.edu/-irenelg/papers/verifcc.ps.gz [La

avid, J. 2005. Lenguaje y nuevas tecnologías: nuevas perspectillsíglo XXI. Madrid: Cátedra.

avoie, B., Kittredge, R. Korelsky, T. and Rambow, O. 2000. «.Based on Uniform Lexico-Structural Processing». .. .

Washington.;. A. 1990. «.WordNet: An On-line Lexical Database». Inte

Langewi: uide to Language Technology, Multilingua,tilingual/downloads/screenSupp65.pdf [Las1

:e. URlCorrectness for a General-PurposE

r.Jatural Language Generation Cantenst visited: 18-8-2006].

'as, métodos y herramientas para el 1,

A Framework for MT and MIn Proceedings of ANLPI

tilingual NLG SystemsIAACL 2000. Seattle,

Miller, (

Natl al Language Generation Systems» [on linej.'www.csd.abdn.ac.uk/-ereiter/papers/jnle97.pdl

,)1 ¿.

Reiter, E. and Dale, R. 1997. «Building AppliedLanguage Engineering. 3. 57-87. URLvisited: 30-8-2006].

Reiter, E. and Dale, R 2000. Building Natt.Press

Rosner, D. and Stede, M 1994. «Gener-project». In Proceedings ofthe In.,

Scott, D. and Evans, R. 1998. «Multilingual

Ifion Sysfems. Cambridge

atingt~matl

multilingual documents from a knowlegde base: the TECHDOCanal Conference on Computational Linguistics (COLlNG), Kyoto.

::ument Management Without Translation: Using naturallanguagegeneration in the Multilingual Information Society» Ion line] Elsnews, 7.1. URL:http://mcs.open.ac.uk/ds5473/publications/IEE-paper.htm [Last visited: 15-8-2006].

liegas, E., Mahesh, K. and Nirenburg, S. 1996. «Semantics in Action». In Proceedings of the Workshop onPredicative Forms in Natural Language and in Knowledge Bases, Toulouse, France. 108-115.

Icock, G. 1993. Interactive Japanese-European Text Generation - an approach to multilinguaJ export translationbased on Systemic Functional Grammar Ion line] Master thesis, University of Manchester Institute 01Science and Technology. URL: http://www.ling.helsinki.fi/-gwilcock/Pubs/1993/MScThesis.pdf [Las1visited: 19-8-2006].

38