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Machine translation using probabilistic synchronous dependency insertion grammars

Authors:

Martha PalmerAuthors Info & Claims

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Pages 541 - 548

https://doi.org/10.3115/1219840.1219907

Published: 25 June 2005 Publication History

Abstract

Syntax-based statistical machine translation (MT) aims at applying statistical models to structured data. In this paper, we present a syntax-based statistical machine translation system based on a probabilistic synchronous dependency insertion grammar. Synchronous dependency insertion grammars are a version of synchronous grammars defined on dependency trees. We first introduce our approach to inducing such a grammar from parallel corpora. Second, we describe the graphical model for the machine translation task, which can also be viewed as a stochastic tree-to-tree transducer. We introduce a polynomial time decoding algorithm for the model. We evaluate the outputs of our MT system using the NIST and Bleu automatic MT evaluation software. The result shows that our system outperforms the baseline system based on the IBM models in both translation speed and quality.

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Cited By

Taufiq UPulungan RSuyanto Y(2023)Named entity recognition and dependency parsing for better concept extraction in summary obfuscation detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119579217:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119579
Al-Thanyyan SAzmi A(2021)Automated Text SimplificationACM Computing Surveys10.1145/344269554:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442695
Jung HLee JMin ENa S(2019)Word Reordering for Translation into Korean Sign Language Using Syntactically-guided ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/335761219:2(1-20)Online publication date: 23-Nov-2019
https://dl.acm.org/doi/10.1145/3357612
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Machine translation using probabilistic synchronous dependency insertion grammars

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cover image DL Hosted proceedings

ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

June 2005

657 pages

General Chair:
Kevin Knight
University of Southern California

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 25 June 2005

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ACL '05 Paper Acceptance Rate 77 of 423 submissions, 18%;

Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

Taufiq UPulungan RSuyanto Y(2023)Named entity recognition and dependency parsing for better concept extraction in summary obfuscation detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119579217:COnline publication date: 1-May-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119579
Al-Thanyyan SAzmi A(2021)Automated Text SimplificationACM Computing Surveys10.1145/344269554:2(1-36)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1145/3442695
Jung HLee JMin ENa S(2019)Word Reordering for Translation into Korean Sign Language Using Syntactically-guided ClassificationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/335761219:2(1-20)Online publication date: 23-Nov-2019
https://dl.acm.org/doi/10.1145/3357612
Jung SPark CLee C(2019)Multitask Pointer Network for Korean Dependency ParsingACM Transactions on Asian and Low-Resource Language Information Processing10.1145/328244218:3(1-10)Online publication date: 8-Feb-2019
https://dl.acm.org/doi/10.1145/3282442
tajner SGlava G(2017)Leveraging event-based semantics for automated text simplificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.04.00582:C(383-395)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.eswa.2017.04.005
Li MWang MLi HXu F(2016)Modeling Monolingual Character Alignment for Automatic Evaluation of Chinese TranslationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/281561915:3(1-18)Online publication date: 28-Jan-2016
https://dl.acm.org/doi/10.1145/2815619
Costa-Jussà MFarrús M(2014)Statistical machine translation enhancements through linguistic levelsACM Computing Surveys10.1145/251813046:3(1-28)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2518130
Goto IUtiyama MSumita E(2013)Post-Ordering by Parsing with ITG for Japanese-English Statistical Machine TranslationACM Transactions on Asian Language Information Processing10.1145/251810012:4(1-22)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2518100
Xiao TZhu JLiu T(2013)Bagging and Boosting statistical machine translation systemsArtificial Intelligence10.1016/j.artint.2012.11.005195(496-527)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.artint.2012.11.005
Ivanova AOepen SØvrelid LFlickinger DIde NXia F(2012)Who did what to whom?Proceedings of the Sixth Linguistic Annotation Workshop10.5555/2392747.2392751(2-11)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2392747.2392751
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