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1
Neural MT and Human Post-editing : a Method to Improve Editorial Quality
In: ISSN: 1134-8941 ; Interlingüística ; https://halshs.archives-ouvertes.fr/halshs-03603590 ; Interlingüística, Alacant [Spain] : Universitat Autònoma de Barcelona, 2022, pp.15-36 (2022)
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2
Human evaluation of three machine translation systems : from quality to attitudes by professional translators
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3
Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
In: Articles (2018)
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4
Human-Guided Evolutionary-Based Linguistics Approach For Automatic Story Generation ...
Wang, Kun. - : UNSW Sydney, 2013
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5
MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing
In: DTIC (2013)
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6
English → Russian MT evaluation campaign
In: ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (2013)
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7
Human-Guided Evolutionary-Based Linguistics Approach For Automatic Story Generation
Wang, Kun, Engineering & Information Technology, UNSW Canberra, UNSW. - : University of New South Wales - UNSW Canberra. Engineering & Information Technology, 2013
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8
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010
Abstract: We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement. ; Johns Hopkins University Human Language Technology Center of Excellence
Keyword: active learning; Amazon Mechanical Turk; annotation bottleneck; annotation cost metrics; annotation costs; artificial intelligence; computational linguistics; computer science; cost-efficient annotation; cost-focused active learning; crowdsourcing; Highlighted N-Gram Method; human language technology; machine learning; machine translation; natural language processing; query learning; selective sampling; statistical machine translation; statistical methods; stopping criteria; stopping methods; translation technology; uncertainty-based active learning; uncertainty-based sampling; Urdu-English translation
URL: http://hdl.handle.net/1903/15549
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9
Task muddiness, intelligence metrics, and the necessity of autonomous mental development
In: http://www.cse.msu.edu/~cse841/papers/MuddyTasks.pdf (2009)
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10
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping ...
Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2009
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11
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2009
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12
Automating Convoy Training Assessment to Improve Soldier Performance
In: DTIC (2008)
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13
Differential Effect of Correct Name Translation on Human and Automated Judgments of Translation Acceptability: A Pilot Study
In: DTIC (2008)
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14
Automatic Computation of . . .
In: http://www.informatik.uni-freiburg.de/~ksimon/papers/CIKM-06-Proximity.pdf (2006)
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15
Toward Joint Segmentation and Classification of Dialog Acts in Multiparty Meetings
In: DTIC (2005)
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16
Symposium on Speech Communication Metrics and Human Performance.
In: DTIC AND NTIS (1995)
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17
Structural analysis of hypertexts: Identifying hierarchies and useful metrics
In: http://www.cs.technion.ac.il/~ehudr/publications/pdf/BotafogoRS92a.pdf (1992)
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18
Natural Language Processing Systems Evaluation Workshop Held in Berkely, California on 18 June 1991
In: DTIC AND NTIS (1991)
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19
Metrics for MT evaluation: Evaluating reordering
In: http://homepages.inf.ed.ac.uk/miles/papers/mt09.pdf
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