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wmt20_mlqe_task3

References:

plain_text

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:wmt20_mlqe_task3/plain_text')
  • Description:
This shared task (part of WMT20) will build on its previous editions
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.

The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.
  • License: Unknown
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 180
'train' 1448
'validation' 200
  • Features:
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            "category": {
                "num_classes": 45,
                "names": [
                    "Addition",
                    "Agreement",
                    "Ambiguous Translation",
                    "Capitalization",
                    "Character Encoding",
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                    "Diacritics",
                    "Duplication",
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                    "Grammatical Register",
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                    "Lexical Register",
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                    "Omitted Auxiliary Verb",
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                    "Omitted Conjunction",
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