wmt20_mlqe_task3

ds = tfds.load('huggingface:wmt20_mlqe_task3/plain_text')

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.

{
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        "_type": "Value"
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    "source_segments": {
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                "names": [
                    "minor",
                    "major",
                    "critical"
                ],
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                "_type": "ClassLabel"
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            "severity_weight": {
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            "category": {
                "num_classes": 45,
                "names": [
                    "Addition",
                    "Agreement",
                    "Ambiguous Translation",
                    "Capitalization",
                    "Character Encoding",
                    "Company Terminology",
                    "Date/Time",
                    "Diacritics",
                    "Duplication",
                    "False Friend",
                    "Grammatical Register",
                    "Hyphenation",
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                    "Shouldn't Have Been Translated",
                    "Shouldn't have been translated",
                    "Spelling",
                    "Tense/Mood/Aspect",
                    "Under-translation",
                    "Unidiomatic",
                    "Unintelligible",
                    "Unit Conversion",
                    "Untranslated",
                    "Whitespace",
                    "Word Order",
                    "Wrong Auxiliary Verb",
                    "Wrong Conjunction",
                    "Wrong Determiner",
                    "Wrong Language Variety",
                    "Wrong Preposition",
                    "Wrong Pronoun"
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                    "critical"
                ],
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                "id": null,
                "_type": "ClassLabel"
            },
            "category": {
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                "names": [
                    "Addition",
                    "Agreement",
                    "Ambiguous Translation",
                    "Capitalization",
                    "Character Encoding",
                    "Company Terminology",
                    "Date/Time",
                    "Diacritics",
                    "Duplication",
                    "False Friend",
                    "Grammatical Register",
                    "Hyphenation",
                    "Inconsistency",
                    "Lexical Register",
                    "Lexical Selection",
                    "Named Entity",
                    "Number",
                    "Omitted Auxiliary Verb",
                    "Omitted Conjunction",
                    "Omitted Determiner",
                    "Omitted Preposition",
                    "Omitted Pronoun",
                    "Orthography",
                    "Other POS Omitted",
                    "Over-translation",
                    "Overly Literal",
                    "POS",
                    "Punctuation",
                    "Shouldn't Have Been Translated",
                    "Shouldn't have been translated",
                    "Spelling",
                    "Tense/Mood/Aspect",
                    "Under-translation",
                    "Unidiomatic",
                    "Unintelligible",
                    "Unit Conversion",
                    "Untranslated",
                    "Whitespace",
                    "Word Order",
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                    "Wrong Conjunction",
                    "Wrong Determiner",
                    "Wrong Language Variety",
                    "Wrong Preposition",
                    "Wrong Pronoun"
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}