Daniel Ortiz-Martínez

Also published as: Daniel Ortiz Martínez, Daniel Ortíz-Martínez


2016

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Online Learning for Statistical Machine Translation
Daniel Ortiz-Martínez
Computational Linguistics, Volume 42, Issue 1 - March 2016

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Beyond Prefix-Based Interactive Translation Prediction
Jesús González-Rubio | Daniel Ortiz-Martínez | Francisco Casacuberta | José Miguel Benedi Ruiz
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

2014

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Efficient wordgraph for interactive translation prediction
Germán Sanchis-Trilles | Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the 17th Annual conference of the European Association for Machine Translation

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Evaluating the effects of interactivity in a post-editing workbench
Nancy Underwood | Bartolomé Mesa-Lao | Mercedes García Martínez | Michael Carl | Vicent Alabau | Jesús González-Rubio | Luis A. Leiva | Germán Sanchis-Trilles | Daniel Ortíz-Martínez | Francisco Casacuberta
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper describes the field trial and subsequent evaluation of a post-editing workbench which is currently under development in the EU-funded CasMaCat project. Based on user evaluations of the initial prototype of the workbench, this second prototype of the workbench includes a number of interactive features designed to improve productivity and user satisfaction. Using CasMaCat’s own facilities for logging keystrokes and eye tracking, data were collected from nine post-editors in a professional setting. These data were then used to investigate the effects of the interactive features on productivity, quality, user satisfaction and cognitive load as reflected in the post-editors’ gaze activity. These quantitative results are combined with the qualitative results derived from user questionnaires and interviews conducted with all the participants.

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Online optimisation of log-linear weights in interactive machine translation
Mara Chinea Rios | Germán Sanchis-Trilles | Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the-art machine translation system underlying an interactive machine translation framework, with encouraging results.

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CASMACAT: A Computer-assisted Translation Workbench
Vicent Alabau | Christian Buck | Michael Carl | Francisco Casacuberta | Mercedes García-Martínez | Ulrich Germann | Jesús González-Rubio | Robin Hill | Philipp Koehn | Luis Leiva | Bartolomé Mesa-Lao | Daniel Ortiz-Martínez | Herve Saint-Amand | Germán Sanchis Trilles | Chara Tsoukala
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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The New Thot Toolkit for Fully-Automatic and Interactive Statistical Machine Translation
Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the Demonstrations at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Integrating online and active learning in a computer-assisted translation workbench
Vicent Alabau | Jesús González-Rubio | Daniel Ortiz-Martínez | Germán Sanchis-Trilles | Francisco Casacuberta | Mercedes García-Martínez | Bartolomé Mesa-Lao | Dan Cheung Petersen | Barbara Dragsted | Michael Carl
Workshop on interactive and adaptive machine translation

This paper describes a pilot study with a computed-assisted translation workbench aiming at testing the integration of online and active learning features. We investigate the effect of these features on translation productivity, using interactive translation prediction (ITP) as a baseline. User activity data were collected from five beta testers using key-logging and eye-tracking. User feedback was also collected at the end of the experiments in the form of retrospective think-aloud protocols. We found that OL performs better than ITP, especially in terms of translation speed. In addition, AL provides better translation quality than ITP for the same levels of user effort. We plan to incorporate these features in the final version of the workbench.

2013

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User Evaluation of Advanced Interaction Features for a Computer-Assisted Translation Workbench
Vicente Alabau | Jesus Gonzalez-Rubio | Luis A. Leiva | Daniel Ortiz-Martínez | German Sanchis-Trilles | Francisco Casacuberta | Bartolomé Mesa-Lao | Ragnar Bonk | Michael Carl | Mercedes Garcia-Martinez
Proceedings of Machine Translation Summit XIV: User track

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Interactive Machine Translation using Hierarchical Translation Models
Jesús González-Rubio | Daniel Ortiz-Martínez | José-Miguel Benedí | Francisco Casacuberta
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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User Evaluation of Interactive Machine Translation Systems
Vincent Alabau | Luis A. Leiva | Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the 16th Annual conference of the European Association for Machine Translation

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Active learning for interactive machine translation
Jesús González-Rubio | Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Bilingual segmentation for phrasetable pruning in Statistical Machine Translation
Germán Sanchis-Trilles | Daniel Ortiz-Martínez | Jesús González-Rubio | Jorge González
Proceedings of the 15th Annual conference of the European Association for Machine Translation

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An Interactive Machine Translation System with Online Learning
Daniel Ortiz-Martínez | Luis A. Leiva | Vicent Alabau | Ismael García-Varea | Francisco Casacuberta
Proceedings of the ACL-HLT 2011 System Demonstrations

2010

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Balancing User Effort and Translation Error in Interactive Machine Translation via Confidence Measures
Jesús González-Rubio | Daniel Ortiz-Martínez | Francisco Casacuberta
Proceedings of the ACL 2010 Conference Short Papers

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On the Use of Confidence Measures within an Interactive-predictive Machine Translation System
Jesús González-Rubio | Daniel Ortíz-Martínez | Francisco Casacuberta
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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Online Learning for Interactive Statistical Machine Translation
Daniel Ortiz-Martínez | Ismael García-Varea | Francisco Casacuberta
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

2009

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Interactive Machine Translation Based on Partial Statistical Phrase-based Alignments
Daniel Ortiz-Martínez | Ismael García-Varea | Francisco Casacuberta
Proceedings of the International Conference RANLP-2009

2008

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Phrase-level alignment generation using a smoothed loglinear phrase-based statistical alignment model
Daniel Ortiz-Martínez | Ismael García-Varea | Francisco Casacuberta
Proceedings of the 12th Annual conference of the European Association for Machine Translation

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Improving Interactive Machine Translation via Mouse Actions
Germán Sanchis-Trilles | Daniel Ortiz-Martínez | Jorge Civera | Francisco Casacuberta | Enrique Vidal | Hieu Hoang
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2006

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Generalized Stack Decoding Algorithms for Statistical Machine Translation
Daniel Ortiz Martínez | Ismael García Varea | Francisco Casacuberta
Proceedings on the Workshop on Statistical Machine Translation

2005

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Thot: a Toolkit To Train Phrase-based Statistical Translation Models
Daniel Ortiz-Martínez | Ismael García-Varea | Francisco Casacuberta
Proceedings of Machine Translation Summit X: Papers

In this paper, we present the Thot toolkit, a set of tools to train phrase-based models for statistical machine translation, which is publicly available as open source software. The toolkit obtains phrase-based models from word-based alignment models; to our knowledge, this functionality has not been offered by any publicly available toolkit. The Thot toolkit also implements a new way for estimating phrase models, this allows to obtain more complete phrase models than the methods described in the literature, including a segmentation length submodel. The toolkit output can be given in different formats in order to be used by other statistical machine translation tools like Pharaoh, which is a beam search decoder for phrase-based alignment models which was used in order to perform translation experiments with the generated models. Additionally, the Thot toolkit can be used to obtain the best alignment between a sentence pair at phrase level.