Using Mechanical Turk to Build Machine Translation Evaluation Sets
dc.contributor.author | Bloodgood, Michael | |
dc.contributor.author | Callison-Burch, Chris | |
dc.date.accessioned | 2014-08-06T01:17:43Z | |
dc.date.available | 2014-08-06T01:17:43Z | |
dc.date.issued | 2010-06 | |
dc.description.abstract | Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon’s Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield. | en_US |
dc.description.sponsorship | This research was supported by the EuroMatrix-Plus project funded by the European Commission, by the DARPA GALE program under Contract No. HR0011-06-2-0001, and the NSF under grant IIS-0713448. Thanks to Amazon Mechanical Turk for providing a $100 credit. | en_US |
dc.identifier.citation | Michael Bloodgood and Chris Callison-Burch. 2010. Using mechanical turk to build machine translation evaluation sets. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, pages 208-211, Los Angeles, California, June. Association for Computational Linguistics. | en_US |
dc.identifier.uri | http://hdl.handle.net/1903/15551 | |
dc.language.iso | en_US | en_US |
dc.publisher | Association for Computational Linguistics | en_US |
dc.relation.isAvailableAt | Center for Advanced Study of Language | |
dc.relation.isAvailableAt | Digitial Repository at the University of Maryland | |
dc.relation.isAvailableAt | University of Maryland (College Park, Md) | |
dc.subject | computer science | en_US |
dc.subject | statistical methods | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | computational linguistics | en_US |
dc.subject | natural language processing | en_US |
dc.subject | human language technology | en_US |
dc.subject | machine translation | en_US |
dc.subject | statistical machine translation | en_US |
dc.subject | machine translation evaluation | en_US |
dc.subject | crowdsourcing | en_US |
dc.subject | Amazon Mechanical Turk | en_US |
dc.subject | cost-efficient annotation | en_US |
dc.subject | annotation costs | en_US |
dc.subject | annotation bottleneck | en_US |
dc.subject | translation costs | en_US |
dc.subject | Urdu-English translation | en_US |
dc.title | Using Mechanical Turk to Build Machine Translation Evaluation Sets | en_US |
dc.type | Article | en_US |
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