Articles

In this paper we describe our participation to the WMT13 Shared Task on Quality Es- timation. The main originality of our ap- proach is to include features originally de- signed to classify text according to some author’s style. This implies the use of ref- erence categories, which are meant to …...

The linguistically transparent MEANT and UMEANT metrics are tunable, simple yet highly effective, fully automatic ap- proximation to the human HMEANT MT evaluation metric which measures seman- tic frame similarity between MT output and reference translations. In this pa- per, we describe HKUST’s submission to the WMT 2013 metrics evaluation …...

We describe TerrorCat, a submission to this year’s metrics shared task. It is a ma- chine learning-based metric that is trained on manual ranking data from WMT shared tasks 2008–2012. Input features are generated by applying automatic transla- tion error analysis to the translation hy- potheses and calculating the error …...

This paper describes the machine learning algorithm and the features used by LIMSI for the Quality Estimation Shared Task. Our submission mainly aims at evaluating the usefulness for quality estimation of n- gram posterior probabilities that quantify the probability for a given n-gram to be part of the system output. …...

We describe the two systems submit- ted by the DCU-Symantec team to Task 1.1. of the WMT 2013 Shared Task on Quality Estimation for Machine Transla- tion. Task 1.1 involve estimating post- editing effort for English-Spanish trans- lation pairs in the news domain. The two systems use a wide variety …...

In this paper we present the system we submitted to the WMT13 shared task on Quality Estimation. We participated in the Task 1.1. Each translated sentence is given a score between 0 and 1. The score is obtained by using several numeri- cal or boolean features calculated accord- ing to …...

This paper presents the LIG’s systems submitted for Task 2 of WMT13 Qual- ity Estimation campaign. This is a word confidence estimation (WCE) task where each participant was asked to la- bel each word in a translated text as a binary ( Keep/Change) or multi-class (Keep/Substitute/Delete) category. We in- tegrate …...

This paper is to describe our machine transla- tion evaluation systems used for participation in the WMT13 shared Metrics Task. In the Metrics task, we submitted two automatic MT evaluation systems nLEPOR_baseline and LEPOR_v3.1. nLEPOR_baseline is an n-gram based language independent MT evaluation metric employing the factors of modified sen- …...

This paper is to introduce our participation in the WMT13 shared tasks on Quality Estima- tion for machine translation without using ref- erence translations. We submitted the results for Task 1.1 (sentence-level quality estima- tion), Task 1.2 (system selection) and Task 2 (word-level quality estimation). In Task 1.1, we used …...

This paper describes the TALP-UPC par- ticipation in the WMT’13 Shared Task on Quality Estimation (QE). Our partic- ipation is reduced to task 1.2 on System Selection. We used a broad set of fea- tures (86 for German-to-English and 97 for English-to-Spanish) ranging from stan- dard QE features to features …...

In this paper we present the approach and system setup of the joint participation of Fondazione Bruno Kessler and University of Edinburgh in the WMT 2013 Quality Estimation shared-task. Our submissions were focused on tasks whose aim was pre- dicting sentence-level Human-mediated Translation Edit Rate and sentence-level post-editing time (Task …...

We introduce referential translation ma- chines (RTM) for quality estimation of translation outputs. RTMs are a computa- tional model for identifying the translation acts between any two data sets with re- spect to a reference corpus selected in the same domain, which can be used for esti- mating the quality …...

We describe the results of our submissions to the WMT13 Shared Task on Quality Estimation (subtasks 1.1 and 1.3). Our submissions use the framework of Gaus- sian Processes to investigate lightweight approaches for this problem. We focus on two approaches, one based on feature se- lection and another based on …...

This paper describes a set of experi- ments on two sub-tasks of Quality Esti- mation of Machine Translation (MT) out- put. Sentence-level ranking of alternative MT outputs is done with pairwise classi- fiers using Logistic Regression with black- box features originating from PCFG Pars- ing, language models and various counts. …...

In this paper we present our entry to the WMT’13 shared task: Quality Estima- tion (QE) for machine translation (MT). We participated in the 1.1, 1.2 and 1.3 sub-tasks with our QE system trained on features from diverse information sources like MT decoder features, n-best lists, mono- and bi-lingual corpora …...

We present Positive Diversity Tuning, a new method for tuning machine translation models specifically for improved perfor- mance during system combination. Sys- tem combination gains are often limited by the fact that the translations produced by the different component systems are too similar to each other. We propose a method …...

We present an iterative technique to gener- ate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Differ- ent from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving to- wards a more principled phrase …...

We present a novel online learning ap- proach for statistical machine translation tailored to the computer assisted transla- tion scenario. With the...

Multi-task learning has been shown to be effective in various applications, including discriminative SMT. We present an exper- imental evaluation of the question whether multi-task learning depends on a “natu- ral” division of data into tasks that bal- ance shared and individual knowledge, or whether its inherent regularization makes multi-task …...

Our field has seen significant improve- ments in the quality of machine translation systems over the past several years. The single biggest factor in this improvement has been the accumulation of ever larger stores of data. However, we now find our- selves the victims of our own success, in that …...