Interactive translation prediction versus conventional post-editing in practice: a study with the CasMaCat workbench

[EN] We conducted a field trial in computer-assisted professional translation to compare interactive translation prediction (ITP) against conventional post-editing (PE) of machine translation (MT) output. In contrast to the conventional PE set-up, where an MT system first produces a static translation hypothesis that is then edited by a professional (hence "post-editing"), ITP constantly updates the translation hypothesis in real time in response to user edits. Our study involved nine professional translators and four reviewers working with the web-based CasMaCat workbench. Various new interactive features aiming to assist the post-editor/translator were also tested in this trial. Our results show that even with little training, ITP can be as productive as conventional PE in terms of the total time required to produce the final translation. Moreover, translation editors working with ITP require fewer key strokes to arrive at the final version of their translation.This work was supported by the European Union’s 7th Framework Programme (FP7/2007–2013) under grant agreement No 287576 (CasMaCat ).Sanchis Trilles, G.; Alabau, V.; Buck, C.; Carl, M.; Casacuberta Nolla, F.; Garcia Martinez, MM.; Germann, U.... (2014). Interactive translation prediction versus conventional post-editing in practice: a study with the CasMaCat workbench. Machine Translation. 28(3-4):217-235. https://doi.org/10.1007/s10590-014-9157-9S217235283-4Alabau V, Leiva LA, Ortiz-Martínez D, Casacuberta F (2012) User evaluation of interactive machine translation systems. 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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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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

Discourse Structure in Machine Translation Evaluation

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse analysis. Computational Linguistics, 201

Machine translation evaluation resources and methods: a survey

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content

The impact of machine translation error types on post-editing effort indicators

In this paper, we report on a post-editing study for general text types from English into Dutch conducted with master's students of translation. We used a fine-grained machine translation (MT) quality assessment method with error weights that correspond to severity levels and are related to cognitive load. Linear mixed effects models are applied to analyze the impact of MT quality on potential post-editing effort indicators. The impact of MT quality is evaluated on three different levels, each with an increasing granularity. We find that MT quality is a significant predictor of all different types of post-editing effort indicators and that different types of MT errors predict different post-editing effort indicators

Referenceless Quality Estimation for Natural Language Generation

Traditional automatic evaluation measures for natural language generation (NLG) use costly human-authored references to estimate the quality of a system output. In this paper, we propose a referenceless quality estimation (QE) approach based on recurrent neural networks, which predicts a quality score for a NLG system output by comparing it to the source meaning representation only. Our method outperforms traditional metrics and a constant baseline in most respects; we also show that synthetic data helps to increase correlation results by 21% compared to the base system. Our results are comparable to results obtained in similar QE tasks despite the more challenging setting.Comment: Accepted as a regular paper to 1st Workshop on Learning to Generate Natural Language (LGNL), Sydney, 10 August 201

English → Russian MT evaluation campaign

This paper presents the settings and the result of the ROMIP 2013 MT shared task for the English→Russian language direction. The quality of generated translations was assessed using automatic metrics and human evaluation. We also discuss ways to reduce human evaluation efforts using pairwise sentence comparisons by human judges to simulate sort operations

Eye-tracking as a measure of cognitive effort for post-editing of machine translation

The three measurements for post-editing effort as proposed by Krings (2001) have been adopted by many researchers in subsequent studies and publications. These measurements comprise temporal effort (the speed or productivity rate of post-editing, often measured in words per second or per minute at the segment level), technical effort (the number of actual edits performed by the post-editor, sometimes approximated using the Translation Edit Rate metric (Snover et al. 2006), again usually at the segment level), and cognitive effort. Cognitive effort has been measured using Think-Aloud Protocols, pause measurement, and, increasingly, eye-tracking. This chapter provides a review of studies of post-editing effort using eye-tracking, noting the influence of publications by Danks et al. (1997), and O’Brien (2006, 2008), before describing a single study in detail. The detailed study examines whether predicted effort indicators affect post-editing effort and results were previously published as Moorkens et al. (2015). Most of the eye-tracking data analysed were unused in the previou