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    <abstract>In this paper, we propose a new method for training translation rules for a Synchronous Context-free Grammar. A bilingual chart parser is used to generate the parse forest, and EM algorithm to estimate expected counts for each rule of the ruleset. Additional rules are constructed as combinations of reliable rules occurring in the parse forest. The new method of proposing additional translation rules is independent of word alignments. We present the theoretical background for this method, and initial experimental results on German-English translations of Europarl data.</abstract>
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%0 Conference Proceedings
%T Enriching SCFG rules directly from efficient bilingual chart parsing
%A Čmejrek, Martin
%A Zhou, Bowen
%A Xiang, Bing
%S Proceedings of the 6th International Workshop on Spoken Language Translation: Papers
%D 2009
%8 dec 1 2
%C Tokyo, Japan
%F cmejrek-etal-2009-enriching
%X In this paper, we propose a new method for training translation rules for a Synchronous Context-free Grammar. A bilingual chart parser is used to generate the parse forest, and EM algorithm to estimate expected counts for each rule of the ruleset. Additional rules are constructed as combinations of reliable rules occurring in the parse forest. The new method of proposing additional translation rules is independent of word alignments. We present the theoretical background for this method, and initial experimental results on German-English translations of Europarl data.
%U https://aclanthology.org/2009.iwslt-papers.2/
%P 136-143

Markdown (Informal)

[Enriching SCFG rules directly from efficient bilingual chart parsing](https://aclanthology.org/2009.iwslt-papers.2/) (Čmejrek et al., IWSLT 2009)

• Enriching SCFG rules directly from efficient bilingual chart parsing (Čmejrek et al., IWSLT 2009)

ACL

• Martin Čmejrek, Bowen Zhou, and Bing Xiang. 2009. Enriching SCFG rules directly from efficient bilingual chart parsing. In Proceedings of the 6th International Workshop on Spoken Language Translation: Papers, pages 136–143, Tokyo, Japan.