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1
Fine-grained evaluation of Quality Estimation for Machine translation based on a linguistically-motivated Test Suite ...
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2
Qualitative: Open Source Python Tool for Quality Estimation over Multiple Machine Translation Outputs
In: Prague Bulletin of Mathematical Linguistics , Vol 102, Iss 1, Pp 5-16 (2014) (2014)
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3
A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/444_Paper.pdf (2012)
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4
A Richly Annotated, Multilingual Parallel Corpus for Hybrid Machine Translation
In: http://www.nclt.dcu.ie/mt/papers/pavel_LREC_2012b.pdf (2012)
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5
Exploiting xle’s finite state interface in lfg-based statistical ma- translation
In: http://www.lfg09.net/abstracts/lfg09abs_avramidis_kuhn.pdf (2009)
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6
Enriching morphologically poor languages for statistical machine translation
In: http://www.mt-archive.info/ACL-2008-Avramidis.pdf (2008)
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7
Enriching Morphologically Poor Languages for Statistical Machine Translation
In: http://aclweb.org/anthology-new/P/P08/P08-1087.pdf (2008)
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8
Evaluation without references: IBM1 scores as evaluation metrics
In: http://www.aclweb.org/anthology-new/W/W11/W11-2109.pdf
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9
The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
In: http://www.nclt.dcu.ie/mt/papers/pavel_LREC_ML4HMT_2012.pdf
Abstract: We describe the “Shared Task on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid Machine Translation ” (ML4HMT) which aims to foster research on improved system combination approaches for machine translation (MT). Participants of the challenge are requested to build hybrid translations by combining the output of several MT systems of different types. We first describe the ML4HMT corpus used in the shared task, then explain the XLIFF-based annotation format we have designed for it, and briefly summarize the participating systems. Using both automated metrics scores and extensive manual evaluation, we discuss the individual performance of the various systems. An interesting result from the shared task is the fact that we were able to observe different systems winning according to the automated metrics scores when compared to the results from the manual evaluation. We conclude by summarising the first edition of the challenge and by giving an outlook to future work.
Keyword: Machine Learning; Machine Translation; System Combination
URL: http://www.nclt.dcu.ie/mt/papers/pavel_LREC_ML4HMT_2012.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.262
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10
The ML4HMT Workshop on Optimising the Division of Labour in Hybrid Machine Translation
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/996_Paper.pdf
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11
The Prague Bulletin of Mathematical Linguistics RankEval: Open Tool for Evaluation of Machine-Learned Ranking
In: http://ufal.mff.cuni.cz/pbml/100/art-avramidis.pdf
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12
Comparative quality estimation: Automatic sentence-level ranking of multiple Machine Translation outputs
In: http://aclweb.org/anthology/C/C12/C12-1008.pdf
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13
Quality Estimation for Machine Translation output using linguistic analysis and decoding features
In: http://www.aclweb.org/anthology/W/W12/W12-3108.pdf
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