A systematic comparison of various statistical alignment models
Computational Linguistics
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Statistical machine translation with word- and sentence-aligned parallel corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Log-linear models for word alignment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discriminative word alignment with conditional random fields
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semi-supervised training for statistical word alignment
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A discriminative matching approach to word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A discriminative framework for bilingual word alignment
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Financial incentives and the "performance of crowds"
Proceedings of the ACM SIGKDD Workshop on Human Computation
Discriminative word alignment via alignment matrix modeling
StatMT '08 Proceedings of the Third Workshop on Statistical Machine Translation
Fast, cheap, and creative: evaluating translation quality using Amazon's Mechanical Turk
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A semi-supervised word alignment algorithm with partial manual alignments
WMT '10 Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR
EMDC: a semi-supervised approach for word alignment
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Crowdsourcing micro-level multimedia annotations: the challenges of evaluation and interface
Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
Proceedings of the 19th international conference on Intelligent User Interfaces
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Word alignment is an important preprocessing step for machine translation. The project aims at incorporating manual alignments from Amazon Mechanical Turk (MTurk) to help improve word alignment quality. As a global crowdsourcing service, MTurk can provide flexible and abundant labor force and there-fore reduce the cost of obtaining labels. An easy-to-use interface is developed to simplify the labeling process. We compare the alignment results by Turkers to that by experts, and incorporate the alignments in a semi-supervised word alignment tool to improve the quality of the labels. We also compared two pricing strategies for word alignment task. Experimental results show high precision of the alignments provided by Turkers and the semi-supervised approach achieved 0.5% absolute reduction on alignment error rate.