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        <title>Reordering Matrix Post-verbal Subjects for Arabic-to-English SMT</title>
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        <namePart type="given">Yuval</namePart>
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    <name type="personal">
        <namePart type="given">Nizar</namePart>
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            <namePart type="given">Philippe</namePart>
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        <name type="personal">
            <namePart type="given">Michel</namePart>
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    <abstract>We improve our recently proposed technique for integrating Arabic verb-subject constructions in SMT word alignment (Carpuat et al., 2010) by distinguishing between matrix (or main clause) and non-matrix Arabic verb-subject constructions. In gold translations, most matrix VS (main clause verb-subject) constructions are translated in inverted SV order, while non-matrix (subordinate clause) VS constructions are inverted in only half the cases. In addition, while detecting verbs and their subjects is a hard task, our syntactic parser detects VS constructions better in matrix than in non-matrix clauses. As a result, reordering only matrix VS for word alignment consistently improves translation quality over a phrase-based SMT baseline, and over reordering all VS constructions, in both medium- and large-scale settings. In fact, the improvements obtained by reordering matrix VS on the medium-scale setting remarkably represent 44% of the gain in BLEU and 51% of the gain in TER obtained with a word alignment training bitext that is 5 times larger.</abstract>
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%0 Conference Proceedings
%T Reordering Matrix Post-verbal Subjects for Arabic-to-English SMT
%A Carpuat, Marine
%A Marton, Yuval
%A Habash, Nizar
%Y Langlais, Philippe
%Y Gagnon, Michel
%S Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs
%D 2010
%8 July
%I ATALA
%C Montréal, Canada
%F carpuat-etal-2010-reordering
%X We improve our recently proposed technique for integrating Arabic verb-subject constructions in SMT word alignment (Carpuat et al., 2010) by distinguishing between matrix (or main clause) and non-matrix Arabic verb-subject constructions. In gold translations, most matrix VS (main clause verb-subject) constructions are translated in inverted SV order, while non-matrix (subordinate clause) VS constructions are inverted in only half the cases. In addition, while detecting verbs and their subjects is a hard task, our syntactic parser detects VS constructions better in matrix than in non-matrix clauses. As a result, reordering only matrix VS for word alignment consistently improves translation quality over a phrase-based SMT baseline, and over reordering all VS constructions, in both medium- and large-scale settings. In fact, the improvements obtained by reordering matrix VS on the medium-scale setting remarkably represent 44% of the gain in BLEU and 51% of the gain in TER obtained with a word alignment training bitext that is 5 times larger.
%U https://aclanthology.org/2010.jeptalnrecital-long.30/
%P 292-301

Markdown (Informal)

[Reordering Matrix Post-verbal Subjects for Arabic-to-English SMT](https://aclanthology.org/2010.jeptalnrecital-long.30/) (Carpuat et al., JEP/TALN/RECITAL 2010)

• Reordering Matrix Post-verbal Subjects for Arabic-to-English SMT (Carpuat et al., JEP/TALN/RECITAL 2010)

ACL

• Marine Carpuat, Yuval Marton, and Nizar Habash. 2010. Reordering Matrix Post-verbal Subjects for Arabic-to-English SMT. In Actes de la 17e conférence sur le Traitement Automatique des Langues Naturelles. Articles longs, pages 292–301, Montréal, Canada. ATALA.