Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-62156
We introduce Addicter, a tool for Automatic Detection and DIsplay of Common Translation ERrors. The tool allows to automatically identify and label translation errors and browse the test and training corpus and word alignments; usage of additional linguistic tools is also supported.
The error classification is inspired by that of Vilar et al. (2006), although some of their higherlevel categories are beyond the reach of the current version of our system. In addition to the tool itself we present a comparison of the proposed method to manually classified translation
errors and a thorough evaluation of the generated alignments.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||06 Faculty of Arts > Institute of Computational Linguistics|
000 Computer science, knowledge & systems
|Deposited On:||09 May 2012 09:18|
|Last Modified:||23 Nov 2012 15:19|
|Funders:||Czech Science Foundation grants P406/11/1499 and P406/10/P259, Estonian Center of Excellence in Computer Science|
|Free access at:||Official URL. An embargo period may apply.|
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