Deliverable D2.2.2 Quality Estimation for Extending Good Translations

We present experiments using quality estimation models to improve the performance of statistical machine translation (SMT) systems by supplementing their training corpora or by building sentence-specific SMT models for instances predicted as having potential for improvement by the ITERPE model. The experiments with quality-informed active learning strategy select, among alternative machine translations, those which are: (i) predicted to have high quality, and thus can be added to the machine translation system training set; (ii) predicted to have low quality, and thus need to be corrected/translated by humans, with the human corrections added to the machine translation system training set. Improvement is measured by the increase in the performance of the overall machine translation systems on held-out datasets, where performance is measured by automatic evaluation metrics comparing the scores of the original machine translation system against the score of the improved machine translation system after additional material is used. The experiments with ITERPE consist in automatically grouping translation instances into different quality bands, for instance for re-translation or for post-editing [Biçici and Specia, 2014]. This method can be helpful in automatic identification of quality barriers in MT to achieve high quality machine translation. This project is funded by the European Union Copyright c © 2014 University of Sheffield Project Final Revision Delivery Date Nature Reviewed By Web links Dissemination QTLaunchPad No. 296347 August 25, 2014 June 30, 2014 Deliverable Maja Popovic (DFKI) http://www.qt21. eu/launchpad/ Public This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). Grant agreement no. 296347 Project acronym QTLaunchPad Project full title Preparation and Launch of a Large-scale Action for Quality Translation Technology Funding scheme Coordination and Support Action Coordinator Prof. Hans Uszkoreit (DFKI) Start date, duration 1 July 2012, 24 months Distribution Public Contractual date of delivery 30 June 2014 Actual date of delivery 30 June 2014 Deliverable number D2.2. 2 Deliverable title Quality Estimation for Extending Good Translations Type Report Status and version Final Number of pages 20 Contributing partners USFD, DCU WP leader USFD Task leader USFD Authors Lucia Specia, Kashif Shah and Ergun Biçici EC project officer Aleksandra Wesolowska The partners in QTLaunchPad are: Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI), Germany Dublin City University (DCU), Ireland Institute for Language and Speech Processing, R.C. “Athena” (ILSP/ATHENA RC), Greece The University of Sheffield (USFD), United Kingdom For copies of reports, updates on project activities and other QTLaunchPad-related information, contact: DFKI GmbH QTLaunchPad Dr. Aljoscha Burchardt aljoscha.burchardt@dfki.de Alt-Moabit 91c Phone: +49 (30) 23895-1838 10559 Berlin, Germany Fax: +49 (30) 23895-1810 Copies of reports and other material can also be accessed via http://www.qt21. eu/launchpad c © 2014, The Individual Authors No part of this document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission from the copyright owner. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).