Test data for the WMT18 QE task. Train data can be downloaded from http://hdl.handle.net/11372/LRT-2619.
This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are:
To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system.
To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time.
To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators.
To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs.
To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments.
In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant.
Training and development data for the WMT18 QE task. Test data will be published as a separate item.
This shared task will build on its previous six editions to further examine automatic methods for estimating the quality of machine translation output at run-time, without relying on reference translations. We include word-level, phrase-level and sentence-level estimation. All tasks make use of datasets produced from post-editions by professional translators. The datasets are domain-specific (IT and life sciences/pharma domains) and extend from those used previous years with more instances and more languages. One important addition is that this year we also include datasets with neural MT outputs. In addition to advancing the state of the art at all prediction levels, our specific goals are:
To study the performance of quality estimation approaches on the output of neural MT systems. We will do so by providing datasets for two language language pairs where the same source segments are translated by both a statistical phrase-based and a neural MT system.
To study the predictability of deleted words, i.e. words that are missing in the MT output. TO do so, for the first time we provide data annotated for such errors at training time.
To study the effectiveness of explicitly assigned labels for phrases. We will do so by providing a dataset where each phrase in the output of a phrase-based statistical MT system was annotated by human translators.
To study the effect of different language pairs. We will do so by providing datasets created in similar ways for four language language pairs.
To investigate the utility of detailed information logged during post-editing. We will do so by providing post-editing time, keystrokes, and actual edits.
Measure progress over years at all prediction levels. We will do so by using last year's test set for comparative experiments.
In-house statistical and neural MT systems were built to produce translations for all tasks. MT system-dependent information can be made available under request. The data is publicly available but since it has been provided by our industry partners it is subject to specific terms and conditions. However, these have no practical implications on the use of this data for research purposes. Participants are allowed to explore any additional data and resources deemed relevant.
Dictionaries with different representations for various languages. Representations include brown clusters of different sizes and morphological dictionaries extracted using different morphological analyzers. All representations cover the most frequent 250,000 word types on the Wikipedia version of the respective language.
Analzers used: MAGYARLANC (Hungarian, Zsibrita et al. (2013)), FREELING (English and Spanish, Padro and Stanilovsky (2012)), SMOR (German, Schmid et al. (2004)), an MA from Charles University (Czech, Hajic (2001)) and LATMOR (Latin, Springmann et al. (2014)).
German has various homophonous sibilant fricatives of phonemic or morphemic nature that can appear in word-final position. In English, the functional status of a word-final \s\ influences its durational properties, with phonemic \s\ being longer than morphemic types. The data set presented here is a small selection of laboratory-elicited German sentences containing various words with final sibilant phonemes (e.g., "das Haus") and morphemes (plural, genitive, clitic, inflection). Durations of the \s\ types were measured and compared across the conditions. An ANOVA between the \s\ types and post-hoc Tukey pair-wise comparisons are presented that show various significant differences.
The submission consists of a csv data file, containing a number of variables, and a PDF document detailing the experiment and variables.
A selection of poetic texts (71,490 words) from the Old English Section of the Helsinki Corpus of English Texts, syntactically and morphologically annotated.
Corpus of the weekly Die Zeit from 1946 - present day (complete runs from 1996). Over 100 million words in 200,000 articles. Updated daily. Part of DWDS project.
Access to full texts (literary, historical, scientific, ... texts); Volltextbibliothek; keine Beschränkung auf literarische Texte (auch z.B. naturwissenschaftliche, geschichtliche, ... Texte)