Articles

This paper describes the features and the ma- chine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features are proposed: one constrained, i.e. respecting the data limitation suggested by the workshop organisers, and one unconstrained, i.e. using data …...

In this paper, we describe the UPC system that participated in the WMT 2012 shared task on Quality Estimation for Machine Translation. Based on the empirical evidence that fluency- related features have a very high correlation with post-editing effort, we present a set of features for the assessment of quality …...

We present the approach we took for our par- ticipation to the WMT12 Quality Estimation Shared Task: our main goal is to achieve rea- sonably good results without appeal to super- vised learning. We have used various simi- larity measures and also an external resource (Google N-grams). Details of results …...

This paper presents techniques for reference- free, automatic prediction of Machine Trans- lation output quality at both sentence- and document-level. In addition to helping with document-level quality estimation, sentence- level predictions are used for system selection, improving the quality of the output transla- tions. We present three system selection tech- …...

In this paper we present the system we sub- mitted to the WMT12 shared task on Quality Estimation. Each translated sentence is given a score between 1 and 5. The score is ob- tained using several numerical or boolean fea- tures calculated according to the source and target sentences. We …...

This paper describes Uppsala University’s submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine re- gression, using a number of explicitly defined features extracted from the Machine Transla- tion input, output and models in combination with tree kernels …...

This is a description of the submissions made by the pattern recognition and human lan- guage technology group (PRHLT) of the Uni- versitat Polit`ecnica de Val`encia to the qual- ity estimation task of the seventh workshop on statistical machine translation (WMT12). We focus on two different issues: how to ef- …...

In this paper we introduce a number of new features for quality estimation in machine translation that were developed for the WMT 2012 quality estimation shared task. We find that very simple features such as indicators of certain characters are able to outperform com- plex features that aim to model …...

We describe a submission to the WMT12 Quality Estimation task, including an exten- sive Machine Learning experimentation. Data were augmented with features from linguis- tic analysis and statistical features from the SMT search graph. Several Feature Selec- tion algorithms were employed. The Quality Estimation problem was addressed both as a …...

This paper describes Stanford University’s sub- mission to the Shared Evaluation Task of WMT 2012. Our proposed metric (SPEDE) com- putes probabilistic edit distance as predictions of translation quality. We learn weighted edit distance in a probabilistic finite state machine (pFSM) model, where state transitions corre- spond to edit operations. …...

We investigate the use of error classification results for automatic evaluation of machine translation output. Five basic error classes are taken into account: morphological errors, syn- tactic (reordering) errors, missing words, ex- tra words and lexical errors. In addition, lin- ear combinations of these categories are in- vestigated. Correlations between …...

A recent paper described a new machine translation evaluation metric, AMBER. This paper describes two changes to AMBER. The first one is incorporation of a new ordering penalty; the second one is the use of the downhill simplex algorithm to tune the weights for the components of AMBER. We tested …...

This paper describes the system used for our participation in the WMT12 Machine Transla- tion evaluation shared task. We also present a new approach to Machine Translation evaluation based on the recently defined task Semantic Textual Similarity. This problem is addressed using a textual entail- ment engine entirely based on …...

This paper describes a study on the contribu- tion of linguistically-informed features to the task of quality estimation for machine trans- lation at sentence level. A standard regression algorithm is used to build models using a com- bination of linguistic and non-linguistic fea- tures extracted from the input text and …...

This paper presents the results of the WMT12 shared tasks, which included a translation task, a task for machine translation evaluation metrics, and a task for run-time estimation of machine translation quality. We conducted a large-scale manual evaluation of 103 machine translation systems submitted by 34 teams. We used the …...

Human assessment is often considered the gold standard in evaluation of translation sys- tems. But in order for the evaluation to be meaningful, the rankings obtained from human assessment must be consistent and repeatable. Recent analysis by Bojar et al. (2011) raised several concerns about the rankings derived from human …...

This paper presents the results of an ex- perimental pilot user study, focusing on the evaluation of machine-translated us- er-generated content by users of an on- line community forum and how those us- ers interact with the MT content that is presented to them. Preliminary results show that ratings are …...

This paper presents a novel efficiency- based evaluation of sentence and word aligners. This assessment is critical in or- der to make a reliable use in industrial sce- narios. The evaluation shows that the re- sources required by aligners differ rather broadly. Subsequently, we establish lim- itation mechanisms on a …...

This paper presents a range of preprocess- ing solutions for Hebrew-English statisti- cal machine translation. Our best system, using a morphological analyzer, increases 3.5 BLEU points over a no-tokenization baseline on a blind test set. The next best system uses Morfessor, an unsupervised morphological segmenter, and obtains al- most 3.0 …...

Unknown words and word segmentation granularity are two main problems in Chinese word segmentation for Chinese- Japanese Machine Translation (MT). In this paper, we propose an approach of exploiting common Chinese characters shared between Chinese and Japanese in Chinese word segmentation optimization for MT aiming to solve these problem- s. …...