liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Iterative reordering and word alignment for statistical MT
Linköping University, Department of Computer and Information Science, NLPLAB - Natural Language Processing Laboratory. Linköping University, The Institute of Technology.
2011 (English)In: Proceedings of the 18th Nordic Conference of Computational Linguistics (NODALIDA 2011) / [ed] Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa, 2011, 315-318 p.Conference paper (Refereed)
Abstract [en]

Word alignment is necessary for statistical machine translation (SMT), and reordering as a preprocessing step has been shown to improve SMT for many language pairs. In this initial study we investigate if both word alignment and reordering can be improved by iterating these two steps, since they both depend on each other. Overall no consistent improvements were seen on the translation task, but the reordering rules contain different information in the different iterations, leading us to believe that the iterative strategy can be useful.

Place, publisher, year, edition, pages
2011. 315-318 p.
, NEALT Proceedings Series, ISSN 1736-6305 ; 11
Keyword [en]
Machine translation, reordering
National Category
Language Technology (Computational Linguistics) Language Technology (Computational Linguistics) Computer Science
URN: urn:nbn:se:liu:diva-70122OAI: diva2:435669
The 18th Nordic Conference of Computational Linguistics, May 11–13, Riga, Latvia
Available from: 2011-08-19 Created: 2011-08-19 Last updated: 2013-07-19Bibliographically approved
In thesis
1. Text Harmonization Strategies for Phrase-Based Statistical Machine Translation
Open this publication in new window or tab >>Text Harmonization Strategies for Phrase-Based Statistical Machine Translation
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis I aim to improve phrase-based statistical machine translation (PBSMT) in a number of ways by the use of text harmonization strategies. PBSMT systems are built by training statistical models on large corpora of human translations. This architecture generally performs well for languages with similar structure. If the languages are different for example with respect to word order or morphological complexity, however, the standard methods do not tend to work well. I address this problem through text harmonization, by making texts more similar before training and applying a PBSMT system.

I investigate how text harmonization can be used to improve PBSMT with a focus on four areas: compounding, definiteness, word order, and unknown words. For the first three areas, the focus is on linguistic differences between languages, which I address by applying transformation rules, using either rule-based or machine learning-based techniques, to the source or target data. For the last area, unknown words, I harmonize the translation input to the training data by replacing unknown words with known alternatives.

I show that translation into languages with closed compounds can be improved by splitting and merging compounds. I develop new merging algorithms that outperform previously suggested algorithms and show how part-of-speech tags can be used to improve the order of compound parts. Scandinavian definite noun phrases are identified as a problem forPBSMT in translation into Scandinavian languages and I propose a preprocessing approach that addresses this problem and gives large improvements over a baseline. Several previous proposals for how to handle differences in reordering exist; I propose two types of extensions, iterating reordering and word alignment and using automatically induced word classes, which allow these methods to be used for less-resourced languages. Finally I identify several ways of replacing unknown words in the translation input, most notably a spell checking-inspired algorithm, which can be trained using character-based PBSMT techniques.

Overall I present several approaches for extending PBSMT by the use of pre- and postprocessing techniques for text harmonization, and show experimentally that these methods work. Text harmonization methods are an efficient way to improve statistical machine translation within the phrase-based approach, without resorting to more complex models.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 95 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1451
Statistical machine translation, text harmonization, compound words, definiteness, reordering, unknown words
National Category
Language Technology (Computational Linguistics) Computer Science General Language Studies and Linguistics
urn:nbn:se:liu:diva-76766 (URN)978-91-7519-887-3 (ISBN)
Public defence
2012-06-11, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2012-05-14 Created: 2012-04-19 Last updated: 2012-05-15Bibliographically approved

Open Access in DiVA

No full text

Other links

Link to article

Search in DiVA

By author/editor
Stymne, Sara
By organisation
NLPLAB - Natural Language Processing LaboratoryThe Institute of Technology
Language Technology (Computational Linguistics)Language Technology (Computational Linguistics)Computer Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 44 hits
ReferencesLink to record
Permanent link

Direct link