PhD thesis - School of Informatics - University of Edinburgh

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Chapter 6. Other Potential Applications 167 GERMAN: Mit diesem Tool können Sie parallel in sämtlichen News suchen. ENGLISH(BABELFISH): With this Tool you can search parallel in all news. FRENCH(BABELFISH): Avec ce Tool, vous pouvez chercher parallèlement tous les News dans. While it was established in Sections 2.1.3.2 and 4.2 that French contains a large number of anglicisms at least in the domain of IT, the use of such anglicisms may, however, not always be the preferred choice by a human translator (HT). They may produce the following French translation of the German sentence: FRENCH(HT): Avec cet outil, vous pouvez chercher tous les actualités en parallèle. Mixed-lingual compounds or interlingual homographs are even more of a challenge to MT systems. One very interesting example occurs in the following German query: 10 GERMAN: Nenne einen Grund für Selbstmord bei Teenagern. ENGLISH(BABELFISH): Call a reason for suicide with dte rodents. FRENCH(BABELFISH): cite une raison de suicide avec des Teenagern. The English inclusion Teenager appears in the dative plural and consequently re- ceives the German inflection n. Instead of treating this noun as an English inclusion when translating the sentence into English, Babelfish processes this token as the Ger- man compound Tee+Nagern (tea + rodents) and translates its subparts into the token dte 11 and the noun rodents. Translating into French, the MT system treats the English inclusion as unseen and inserts it directly into the translation without further processing, such as inflection removal. Combined with a named entity recogniser, the English inclusion classifier could signal to the MT engine which items require either translating or transferring with respect to the target language. Multi-word English inclusions also pose difficulty to most MT systems. If the system has not encountered a particular expression in its training data or its lexicon, it is likely to treat the entire expression as unseen. However, MT systems are not necessarily aware of the boundaries of such multi-word expression as illustrated in 10This query appeared in CLEF 2004 where one of the tasks was to find answers to German question in English documents (Ahn et al., 2004). 11Note that dte is not a typo but an error in the MT output.
Chapter 6. Other Potential Applications 168 the following German example sentence, its English gloss and translations by ten MT system demos currently available online. GERMAN: Made in Germany ist gefragt. ENGLISH(HT): Made in Germany is in demand. ENGLISH(LOCAL TRANSLATION): 10 Larva in Germany is in demand. ✗ ENGLISH(COMPENDRIUM): 11 Maggot in Germany is in demand. ✗ ENGLISH(FREETRANSLATION.COM): 12 Maggot in Germany is asked ✗ ENGLISH(HYPERTRANS): 13 Grub in Germany is asked. ✗ ENGLISH(INTERTRAN): 14 maggot in Teuton am asked. ✗ ENGLISH(PERSONAL TRANSLATOR): 15 Maggot in Germany is in demand. ✗ ENGLISH(POWER TRANSLATOR): 16 Made in Germany is in demand. ✓ ENGLISH(REVERSO): 17 Maggot in Germany is asked. ✗ ENGLISH(SYSTRAN): 18 Larva in Germany is in demand. ✗ ENGLISH(@PROMT): 19 Maggot in Germany is asked. ✗ The translations by the different MT engines show that most systems (9/10) strug- gled to transfer the multi-word English inclusion back into English. Only one system (POWER TRANSLATOR) produced a correct translation, identical to the human render- ing. When surrounding the English inclusion by quotation marks in the German input sentence, one other system (HYPERTRANS) was able to translate the expression correctly. All other systems produced the same wrong translation as already listed. It can be seen that the majority of systems (5/9), that did not produce a correct translation, managed to process the German portion of the sentence correctly but failed on the English inclusion. Most systems failed to recognise the entire English phrase as unseen. Instead of transferring it, they mistook Made, the first word of the inclusion, as the German noun Made (maggot, larva or grub). Only one system (INTERTRAN) 10 http://www.localtranslation.com/ 11 http://www.translendium.com/ 12 http://www.freetranslation.com/ 13 http://www.dagostini.it/hypertrans/index.php 14 http://www.tranexp.com:2000/Translate/result.shtml 15 http://www.linguatec.net/onlineservices/pt 16 http://www.lec.com/w2/translate-demos.asp 17 http://www.reverso.net/text_translation.asp?lang=EN 18 http://www.systran.co.uk/ 19 http://www.e-promt.com/
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Automatic Detection of English Incl
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these parsers with the annotation-f
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Declaration I declare that this the
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3.3.5 Post-processing Module . . .
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A.2.2 Kappa Coefficient . . . . . .
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5.6 Average relative token frequenc
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3.16 Most frequent English inclusio
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Chapter 1. Introduction 2 siderable
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Chapter 1. Introduction 4 Chapter 3
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Chapter 1. Introduction 6 1.1 Relat
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Chapter 2. Background and Theory 8
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Page 211 and 212: Bibliography 198 Andersen, G. (2005
Page 213 and 214: Bibliography 200 Bresnan, J. (2001)
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Page 219 and 220: Bibliography 206 Hachey, B., Alex,
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Page 225 and 226: Bibliography 212 Poplack, S. (1988)
Page 227 and 228: Bibliography 214 Sokol, D. K. (2000
Page 229: Bibliography 216 Yang, W. (1990). A
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PhD thesis - School of Informatics - University of Edinburgh

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