poster

An active learning scenario for interactive machine translation

Authors:

Jesús González-Rubio,

Daniel Ortiz-Martínez,

Francisco CasacubertaAuthors Info & Claims

ICMI '11: Proceedings of the 13th international conference on multimodal interfaces

Pages 197 - 200

https://doi.org/10.1145/2070481.2070514

Published: 14 November 2011 Publication History

Abstract

This paper provides the first experimental study of an active learning (AL) scenario for interactive machine translation (IMT). Unlike other IMT implementations where user feedback is used only to improve the predictions of the system, our IMT implementation takes advantage of user feedback to update the statistical models involved in the translation process. We introduce a sentence sampling strategy to select the sentences that are worth to be interactively translated, and a retraining method to update the statistical models with the user-validated translations. Both, the sampling strategy and the retraining process are designed to work in real-time to meet the severe time constraints inherent to the IMT framework. Experiments in a simulated setting showed that the use of AL dramatically reduces user effort required to obtain translations of a given quality.

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Cited By

Mai YYuan X(2024)RETRACTED ARTICLE: Deep learning based optical network transmission application in Chinese English translation system in cloud computing environmentOptical and Quantum Electronics10.1007/s11082-024-06297-856:4Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11082-024-06297-8
Mendonça VRei RCoheur LSardinha A(2023) Onception : Active Learning with Expert Advice for Real World Machine Translation Computational Linguistics10.1162/coli_a_0047349:2(325-372)Online publication date: 1-Jun-2023
https://doi.org/10.1162/coli_a_00473
Ye NXu PWu CZhang G(2018)Using Bilingual Segments to Improve Interactive Machine TranslationNatural Language Processing and Chinese Computing10.1007/978-3-319-73618-1_22(255-266)Online publication date: 5-Jan-2018
https://doi.org/10.1007/978-3-319-73618-1_22
Show More Cited By

Index Terms

An active learning scenario for interactive machine translation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Machine translation
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools

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Information

Published In

cover image ACM Conferences

ICMI '11: Proceedings of the 13th international conference on multimodal interfaces

November 2011

432 pages

ISBN:9781450306416

DOI:10.1145/2070481

General Chairs:
Hervé Bourlard
Idiap Research Institute, Switzerland
,
Thomas S. Huang
University of Illinois, USA
,
Enrique Vidal
Universitat Politécnica Valéncia, Spain
,
Program Chairs:
Daniel Gatica-Perez
Idiap Research Institute, Switzerland
,
Louis-Philippe Morency
University of Southern California, USA
,
Nicu Sebe
University of Trento, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 November 2011

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ICMI'11

Sponsor:

SIGCHI

ICMI'11: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 14 - 18, 2011

Alicante, Spain

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Citations

Cited By

Mai YYuan X(2024)RETRACTED ARTICLE: Deep learning based optical network transmission application in Chinese English translation system in cloud computing environmentOptical and Quantum Electronics10.1007/s11082-024-06297-856:4Online publication date: 31-Jan-2024
https://doi.org/10.1007/s11082-024-06297-8
Mendonça VRei RCoheur LSardinha A(2023) Onception : Active Learning with Expert Advice for Real World Machine Translation Computational Linguistics10.1162/coli_a_0047349:2(325-372)Online publication date: 1-Jun-2023
https://doi.org/10.1162/coli_a_00473
Ye NXu PWu CZhang G(2018)Using Bilingual Segments to Improve Interactive Machine TranslationNatural Language Processing and Chinese Computing10.1007/978-3-319-73618-1_22(255-266)Online publication date: 5-Jan-2018
https://doi.org/10.1007/978-3-319-73618-1_22
O'Brien S(2017)Machine Translation and CognitionThe Handbook of Translation and Cognition10.1002/9781119241485.ch17(311-331)Online publication date: 18-Feb-2017
https://doi.org/10.1002/9781119241485.ch17
Ortiz-Martínez D(2016)Online Learning for Statistical Machine TranslationComputational Linguistics10.1162/COLI_a_0024442:1(121-161)Online publication date: Mar-2016
https://doi.org/10.1162/COLI_a_00244
Björklund JFernau H(2016)Learning Tree LanguagesTopics in Grammatical Inference10.1007/978-3-662-48395-4_7(173-213)Online publication date: 5-May-2016
https://doi.org/10.1007/978-3-662-48395-4_7
Ortiz-Martínez DGonzález-Rubio JAlabau VSanchis-Trilles GCasacuberta F(2016)Integrating Online and Active Learning in a Computer-Assisted Translation WorkbenchNew Directions in Empirical Translation Process Research10.1007/978-3-319-20358-4_3(57-76)Online publication date: 2016
https://doi.org/10.1007/978-3-319-20358-4_3
González-Rubio JCasacuberta F(2014)Cost-sensitive active learning for computer-assisted translationPattern Recognition Letters10.1016/j.patrec.2013.06.00737(124-134)Online publication date: Feb-2014
https://doi.org/10.1016/j.patrec.2013.06.007
González-Rubio JOrtiz-Martínez DCasacuberta FDaelemans W(2012)Active learning for interactive machine translationProceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics10.5555/2380816.2380849(245-254)Online publication date: 23-Apr-2012
https://dl.acm.org/doi/10.5555/2380816.2380849

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