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A Comparison of Merging Strategies for Translation of German Compounds
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
2009 (English)In: Proceedings of the Student Research Workshop at the 12th Conference of the European Chapter of the ACL (EACL 2009), Association for Computational Linguistics , 2009, 61-69 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this article, compound processing for translation into German in a factored statistical MT system is investigated. Compound sare handled by splitting them prior to training, and merging the parts after translation. I have explored eight merging strategies using different combinations of external knowledge sources, such as word lists, and internal sources that are carried through the translation process, such as symbols or parts-of-speech. I show that for merging to be successful, some internal knowledge source is needed. I also show that an extra sequence model for part-ofspeech is useful in order to improve the order of compound parts in the output. The best merging results are achieved by a matching scheme for part-of-speech tags.

Place, publisher, year, edition, pages
Association for Computational Linguistics , 2009. 61-69 p.
Keyword [en]
Natural language processing, machine translation, compounds
National Category
Computer Science Language Technology (Computational Linguistics) Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-20318OAI: oai:DiVA.org:liu-20318DiVA: diva2:233949
Available from: 2009-09-03 Created: 2009-09-03 Last updated: 2009-12-07Bibliographically approved
In thesis
1. Compound Processing for Phrase-Based Statistical Machine Translation
Open this publication in new window or tab >>Compound Processing for Phrase-Based Statistical Machine Translation
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis I explore how compound processing can be used to improve phrase-based statistical machine translation (PBSMT) between English and German/Swedish. Both German and Swedish generally use closed compounds, which are written as one word without spaces or other indicators of word boundaries. Compounding is both common and productive, which makes it problematic for PBSMT, mainly due to sparse data problems.

The adopted strategy for compound processing is to split compounds into their component parts before training and translation. For translation into Swedish and German the parts are merged after translation. I investigate the effect of different splitting algorithms for translation between English and German, and of different merging algorithms for German. I also apply these methods to a different language pair, English--Swedish. Overall the studies show that compound processing is useful, especially for translation from English into German or Swedish. But there are improvements for translation into English as well, such as a reduction of unknown words.

I show that for translation between English and German different splitting algorithms work best for different translation directions. I also design and evaluate a novel merging algorithm based on part-of-speech matching, which outperforms previous methods for compound merging, showing the need for information that is carried through the translation process, rather than only external knowledge sources such as word lists. Most of the methods for compound processing were originally developed for German. I show that these methods can be applied to Swedish as well, with similar results.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 64 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1421
Keyword
Machine translation, compounds, factored translation, statistical machine translation, computational linguistics
National Category
Language Technology (Computational Linguistics) Language Technology (Computational Linguistics) Computer Science
Identifiers
urn:nbn:se:liu:diva-51416 (URN)978-91-7393-501-2 (ISBN)
Presentation
2009-12-18, Alan Turing, Hus E, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2009-12-07 Created: 2009-10-30 Last updated: 2009-12-07Bibliographically approved

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Stymne, Sara

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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