Efficient Combination of Confidence Measures for Machine Translation

International audienceWe present in this paper a twofold contribution to Confidence Measures for Machine Translation. First, in order to train and test confidence measures, we present a method to automatically build corpora containing realistic errors. Errors introduced into reference translation simulate classical machine translation errors (word deletion and word substitution), and are supervised by Wordnet. Second, we use SVM to combine original and classical confidence measures both at word- and sentence-level. We show that the obtained combination outperforms by 14% (absolute) our best single word-level confidence measure, and that combination of sentence-level confidence measures produces meaningful scores

Word- and sentence-level confidence measures for machine translation

International audienceA machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an estimation of the probability of correctness. In this article we describe several word- and sentence-level confidence measures relying on different features: mutual information between words, n-gram and backward n-gram language models, and linguistic features. We also try different combination of these measures. Their accuracy is evaluated on a classification task. We achieve 17% error-rate (0.84 f-measure) on word-level and 31% error-rate (0.71 f-measure) on sentence-level

New Confidence Measures for Statistical Machine Translation

International audienceA confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1\% with an F-measure of 0.708

Segment-based interactive-predictive machine translation

[EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort.The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030).Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3S163185314Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. 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Partial least squares for word confidence estimation in machine translation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38628-2_59We present a new technique to estimate the reliability of the words in automatically generated translations. Our approach addresses confidence estimation as a classification problem where a confidence score is to be predicted from a feature vector that represents each translated word. We describe a new set of prediction features designed to capture context information, and propose a model based on partial least squares to perform the classification. Good empirical results are reported in a large-domain news translation task.Work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).González Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Partial least squares for word confidence estimation in machine translation. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 500-508. https://doi.org/10.1007/978-3-642-38628-2_59S500508NIST: National Institute of Standards and Technology MT evaluation official results (November 2006), http://www.itl.nist.gov/iad/mig/tests/mt/Ueffing, N., Macherey, K., Ney, H.: Confidence measures for statistical machine translation. In: Proc. of the MT Summit, pp. 394–401. Springer (2003)Sanchis, A., Juan, A., Vidal, E.: Estimation of confidence measures for machine translation. In: Proc. of the Machine Translation Summit, pp. 407–412 (2007)Wold, H.: Estimation of Principal Components and Related Models by Iterative Least squares, pp. 391–420. Academic Press, New York (1966)Berger, A.L., Pietra, V.J.D., Pietra, S.A.D.: A maximum entropy approach to natural language processing. Computational Linguistics 22, 39–71 (1996)Levenshtein, V.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10(8), 707–710 (1966)Brown, P., Della Pietra, V., Della Pietra, S., Mercer, R.: The mathematics of statistical machine translation: parameter estimation. Computational Linguistics 19, 263–311 (1993)Mevik, B.H., Wehrens, R., Liland, K.H.: pls: Partial Least Squares and Principal Component regression. R package version 2.3-0 (2011)Callison-Burch, C., Koehn, P., Monz, C., Post, M., Soricut, R., Specia, L.: Findings of the 2012 workshop on statistical machine translation. In: Proc. of the Workshop on Statistical Machine Translation, Montréal, Canada, pp. 10–51 (June 2012)Chinchor, N.: The statistical significance of the muc-4 results. In: Proceedings of the Conference on Message Understanding, pp. 30–50 (1992

Dimensionality reduction methods for machine translation quality estimation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-013-9139-3[EN] Quality estimation (QE) for machine translation is usually addressed as a regression problem where a learning model is used to predict a quality score from a (usually highly-redundant) set of features that represent the translation. This redundancy hinders model learning, and thus penalizes the performance of quality estimation systems. We propose different dimensionality reduction methods based on partial least squares regression to overcome this problem, and compare them against several reduction methods previously used in the QE literature. Moreover, we study how the use of such methods influence the performance of different learning models. Experiments carried out on the English-Spanish WMT12 QE task showed that it is possible to improve prediction accuracy while significantly reducing the size of the feature sets.This work supported by the European Union Seventh Framework Program (FP7/2007-2013) under the CasMaCat project (grants agreement no. 287576), by Spanish MICINN under TIASA (TIN2009-14205-C04-02) project, and by the Generalitat Valenciana under grant ALMPR (Prometeo/2009/014).González Rubio, J.; Navarro Cerdán, JR.; Casacuberta Nolla, F. (2013). Dimensionality reduction methods for machine translation quality estimation. Machine Translation. 27(3-4):281-301. https://doi.org/10.1007/s10590-013-9139-3S281301273-4Amaldi E, Kann V (1998) On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor Comput Sci 209(1–2):237–260Anderson TW (1958) An introduction to multivariate statistical analysis. 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"This sentence is wrong." Detecting errors in machine-translated sentences.

International audienceMachine translation systems are not reliable enough to be used ''as is'': except for the most simple tasks, they can only be used to grasp the general meaning of a text or assist human translators. The purpose of confidence measures is to detect erroneous words or sentences produced by a machine translation system. In this article after reviewing the mathematical foundations of confidence estimation we propose a comparison of several state-of-the-art confidence measures, predictive parameters and classifiers. We also propose two original confidence measures based on Mutual Information and a method for automatically generating data for training and testing classifiers. We applied these techniques to data from WMT campaign 2008 and found that the best confidence measures yielded an Equal Error Rate of 36.3% at word level and 34.2% at sentence level, but combining different measures reduced these rates to respectively 35.0\% and 29.0\%. We also present the results of an experiment aimed at determining how helpful confidence measures are in a post edition task. Preliminary results suggest that our system is not yet ready to efficiently help post editors, but we now have a software and protocol we can apply to further experiments, and user feedback has indicated aspects which must be improved in order to increase the level of helpfulness of confidence measures

Towards predicting post-editing productivity

Machine translation (MT) quality is generally measured via automatic metrics, producing scores that have no meaning for translators who are required to post-edit MT output or for project managers who have to plan and budget for transla- tion projects. This paper investigates correlations between two such automatic metrics (general text matcher and translation edit rate) and post-editing productivity. For the purposes of this paper, productivity is measured via processing speed and cognitive measures of effort using eye tracking as a tool. Processing speed, average fixation time and count are found to correlate well with the scores for groups of segments. Segments with high GTM and TER scores require substantially less time and cognitive effort than medium or low-scoring segments. Future research involving score thresholds and confidence estimation is suggested