PE2 rr corpus: manual error annotation of automatically pre-annotated MT post-edits
Date
2016-05-23Author
Popovic, Maja
Arcan, Mihael
Metadata
Show full item recordUsage
This item's downloads: 66 (view details)
Recommended Citation
Popovic, Maja, & Arcan, Mihael. (2016). PE2rr corpus: manual error annotation of automatically pre-annotated MT post-edits. Paper presented at the LREC 2016, Tenth International Conference on Language Resources and Evaluation, Portorož, Slovenia, 23-28 May.
Published Version
Abstract
We present a freely available corpus containing source language texts from different domains along with their automatically generated
translations into several distinct morphologically rich languages, their post-edited versions, and error annotations of the performed
post-edit operations. We believe that the corpus will be useful for many different applications. The main advantage of the approach used
for creation of the corpus is the fusion of post-editing and error classification tasks, which have usually been seen as two independent
tasks, although naturally they are not. We also show benefits of coupling automatic and manual error classification which facilitates
the complex manual error annotation task as well as the development of automatic error classification tools. In addition, the approach
facilitates annotation of language pair related issues.
Collections
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Ireland
Related items
Showing items related by title, author, creator and subject.
-
A comparison of emotion annotation approaches for text
Wood, Ian; McCrae, John; Andryushechkin, Vladimir; Buitelaar, Paul (MDPI AG, 2018-05-11)While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there ... -
A comparison of emotion annotation approaches for text
Wood, Ian D.; McCrae, John P.; Andryushechkin, Vladimir; Buitelaar, Paul (MDPI, 2018-05-11)While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there ... -
A comparison of emotion annotation schemes and a new annotated data set
Wood, Ian D.; McCrae, John P.; Andryushechkin, Vladimir; Buitelaar, Paul (European Languages Resources Association (ELRA), 2018-05-07)While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of more nuanced affect has received less attention, and ...