Sentence-level Post-Editing Effort QE shared task 2020

Organized by fblain - Current server time: Aug. 7, 2020, 9:21 p.m. UTC

Previous

English-German
April 19, 2020, midnight UTC

Current

English-Chinese
April 19, 2020, midnight UTC

End

Competition Ends
Never

QE Shared Task 2020

The official shared task on Quality Estimation aims to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.

In addition to generally advancing the state of the art at all prediction levels for modern neural MT, our specific goals are:

  • to create a new set of public benchmarks for tasks in quality estimation,
  • to investigate models for predicting DA scores and their relationship with models trained for predicting post-editing effort,
  • to study the feasibility of mulilingual (or even language independent) approaches to QE, and
  • to study the influence of source-language document-level context for the task of QE, and
  • to analyse the aplicabiity of NMT model information for QE.

Offical task webpage: QE Shared Task 2020

This submission platform covers Task 2: Sentence-level *Post-Editing Effort*.

In Task 2, participating systems are required to score sentences according to HTER scores. Submissions will be evaluated according to how well they score translations. We thus expect an absolute quality score for each sentence translation.

Submission Format

The output of your system for a given subtask should produce scores for the translations at the segment-level formatted in the following way:

<LANGUAGE PAIR> <METHOD NAME> <SEGMENT NUMBER> <SEGMENT SCORE>

Where:

  • LANGUAGE PAIR is the ID (e.g., en-de) of the language pair of the plain text translation file you are scoring.
  • METHOD NAME is the name of your quality estimation method.
  • SEGMENT NUMBER is the line number of the plain text translation file you are scoring.
  • SEGMENT SCORE is the predicted score for the particular segment.

Each field should be delimited by a single tab character.

To allow the automatic evaluation of your predictions, please submit them in a file named as follows: predictions.txt, and package them in a zipped file (.zip).

Submissions will be evaluated in terms of the Pearson's correlation metric for the sentence-level HTER prediction.

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.

The provided QE labelled data is publicly available under Creative Commons Attribution Share Alike 4.0 International (https://github.com/facebookresearch/mlqe/blob/master/LICENSE).

Participants are allowed to explore any additional data and resources deemed relevant.

Each participating team can submit at most 30 systems for each of the language pairs of each subtask (max 5 a day).

English-German

Start: April 19, 2020, midnight

English-Chinese

Start: April 19, 2020, midnight

Competition Ends

Never

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