Type

Conference Proceedings

Authors

Andy Way
Ventsislav Zhechev
John Tinsley
Mary Hearne

Subjects

Computer Science

Topics
algorithm tree alignment parallel treebanks word alignment statistical machine translation tree source machine translating

Capturing translational divergences with a statistical tree-to-tree aligner (2007)

Abstract Parallel treebanks, which comprise paired source-target parse trees aligned at sub-sentential level, could be useful for many applications, particularly data-driven machine translation. In this paper, we focus on how translational divergences are captured within a parallel treebank using a fully automatic statistical tree-to-tree aligner. We observe that while the algorithm performs well at the phrase level, performance on lexical-level alignments is compromised by an inappropriate bias towards coverage rather than precision. This preference for high precision rather than broad coverage in terms of expressing translational divergences through tree-alignment stands in direct opposition to the situation for SMT word-alignment models. We suggest that this has implications not only for tree-alignment itself but also for the broader area of induction of syntaxaware models for SMT.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: National Centre for Language Technology (NCLT)
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres

Full list of authors on original publication

Andy Way, Ventsislav Zhechev, John Tinsley, Mary Hearne

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
John Tinsley
Dublin City University
Total Publications: 13