Relevance Ranking for Translated Texts

Relevance Ranking for Translated Texts

The usefulness of a translated text for gisting purposes strongly depends on the overall translation quality of the text, but especially on the translation quality of the most informative portions of the text. In this paper we address the problems of ranking translated sentences within a document and ranking translated documents within a set of documents on the same topic according to their informativeness and translation quality. An approach combining quality estimation and sentence ranking methods is used. Experiments with French-English translation using four sets of news commentary documents show promising results for both sentence and document ranking. %, outperforming different baseline methods.We believe that this approach can be useful in several practical scenarios where translation is aimed at gisting, such as multilingual media monitoring and news analysis applications.

Keywords: Machine translation, translation quality estimation, sentence ranking based on informativeness

Year: 2012

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Authors of this publication:

Josef Steinberger


Josef is an associated professor at the Department of computer science and engineering at the University of West Bohemia in Pilsen, Czech Republic. He is interested in media monitoring and analysis, mainly automatic text summarisation, sentiment analysis and coreference resolution.

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Automatic Text Summarisation

Authors:  Josef Steinberger, Karel Je┼żek, Michal Campr, Ji┼Ö├ş Hynek
Desc.:Automatic text summarisation using various text mining methods, mainly Latent Semantic Analysis (LSA).