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Machine translation divergences: a formal description and proposed solution

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Bonnie J. DorrAuthors Info & Claims

Computational Linguistics, Volume 20, Issue 4

Pages 597 - 633

Published: 01 December 1994 Publication History

Abstract

There are many cases in which the natural translation of one language into another results in a very different form than that of the original. The existence of translation divergences (i.e., crosslinguistic distinctions) makes the straightforward transfer from source structures into target structures impractical. Many existing translation systems have mechanisms for handling divergent structures but do not provide a general procedure that takes advantage of takes advantage of the systematic relation between lexical-semantic structure and syntactic structure. This paper demonstrates that a systematic solution to the divergence problem can be derived from the formalization of two types of information: (1) the linguistically grounded classes upon which lexical-semantic divergences are based; and (2) the techniques by which lexical-semantic divergences are resolved. This formalization is advantageous in that it facilitates the design and implementation of the system, allows one to make an evaluation of the status of the system, and provides a basis for proving certain important properties about the system.

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cover image Computational Linguistics

Computational Linguistics Volume 20, Issue 4

December 1994

170 pages

ISSN:0891-2017

EISSN:1530-9312

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MIT Press

Cambridge, MA, United States

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Published: 01 December 1994

Published in COLI Volume 20, Issue 4

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S SBhattacharyya P(2017)Role of Morphology Injection in SMTACM Transactions on Asian and Low-Resource Language Information Processing10.1145/312920817:1(1-31)Online publication date: 15-Sep-2017
https://dl.acm.org/doi/10.1145/3129208
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https://dl.acm.org/doi/10.1007/s10579-016-9362-7
Xia FLewis WGoodman MSlayden GGeorgi RCrowgey JBender E(2016)Enriching a massively multilingual database of interlinear glossed textLanguage Resources and Evaluation10.1007/s10579-015-9325-450:2(321-349)Online publication date: 1-Jun-2016
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Zhang MXiao XXiong DLiu Q(2014)Topic-based dissimilarity and sensitivity models for translation rule selectionJournal of Artificial Intelligence Research10.5555/2693068.269306950:1(1-30)Online publication date: 1-May-2014
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