NDLI: Hybrid data-driven models of machine translation

Please wait, while we are loading the content...

Hybrid data-driven models of machine translation

Content Provider	Springer Nature Link
Author	Groves, Declan Way, Andy
Copyright Year	2006
Abstract	This paper presents an extended, harmonised account of our previous work on combining subsentential alignments from phrase-based statistical machine translation (SMT) and example-based MT (EBMT) systems to create novel hybrid data-driven systems capable of outperforming the baseline SMT and EBMT systems from which they were derived. In previous work, we demonstrated that while an EBMT system is capable of outperforming a phrase-based SMT (PBSMT) system constructed from freely available resources, a hybrid ‘example-based’ SMT system incorporating marker chunks and SMT subsentential alignments is capable of outperforming both baseline translation models for French–English translation. In this paper, we show that similar gains are to be had from constructing a hybrid ‘statistical’ EBMT system. Unlike the previous research, here we use the Europarl training and test sets, which are fast becoming the standard data in the field. On these data sets, while all hybrid ‘statistical’ EBMT variants still fall short of the quality achieved by the baseline PBSMT system, we show that adding the marker chunks to create a hybrid ‘example-based’ SMT system outperforms the two baseline systems from which it is derived. Furthermore, we provide further evidence in favour of hybrid systems by adding an SMT target-language model to the EBMT system, and demonstrate that this too has a positive effect on translation quality. We also show that many of the subsentential alignments derived from the Europarl corpus are created by either the PBSMT or the EBMT system, but not by both. In sum, therefore, despite the obvious convergence of the two paradigms, the crucial differences between SMT and EBMT contribute positively to the overall translation quality. The central thesis of this paper is that any researcher who continues to develop an MT system using either of these approaches will benefit further from integrating the advantages of the other model; dogged adherence to one approach will lead to inferior systems being developed.
Starting Page	301
Ending Page	323
Page Count	23
File Format	PDF
ISSN	09226567
Journal	Machine Translation
Volume Number	19
Issue Number	3-4
e-ISSN	15730573
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2006-11-02
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Hybrid Example-based MT Statistical MT Statistical language models Convergence Chunk coverage Europarl corpus Artificial Intelligence (incl. Robotics) Language Translation and Linguistics Computational Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software Linguistics and Language

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

MT model space: statistical versus compositional versus example-based machine translation

A hybrid machine translation architecture guided by syntax

Dependency treelet translation: the convergence of statistical and example-based machine-translation?

Chunk-lattices for verb reordering in Arabic–English statistical machine translation : Special issues on machine translation for Arabic

Inducing translation templates with type constraints

Example-based machine translation: a review and commentary

The Long-Term Forecast for Weather Bulletin Translation

Example-based machine translation based on tree–string correspondence and statistical generation

EBMT by tree-phrasing

Hybrid data-driven models of machine translation

Similar Documents

MT model space: statistical versus compositional versus example-based machine translation

A hybrid machine translation architecture guided by syntax

Dependency treelet translation: the convergence of statistical and example-based machine-translation?

Chunk-lattices for verb reordering in Arabic–English statistical machine translation : Special issues on machine translation for Arabic

Inducing translation templates with type constraints

Example-based machine translation: a review and commentary

The Long-Term Forecast for Weather Bulletin Translation

Example-based machine translation based on tree–string correspondence and statistical generation

EBMT by tree-phrasing

Hybrid data-driven models of machine translation