WMT English-French dataset
E899019
The WMT English-French dataset is a large-scale parallel corpus of English–French sentence pairs widely used as a benchmark for training and evaluating machine translation systems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| WMT English-French dataset canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T11003131 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WMT English-French dataset Context triple: [Sequence to Sequence Learning with Neural Networks, demonstratedOn, WMT English-French dataset]
-
A.
CORDE corpus
The CORDE corpus is a large historical Spanish language corpus compiled by the Royal Spanish Academy, used for studying the evolution and usage of Spanish over time.
-
B.
WMT
WMT is the stock ticker symbol for Walmart Inc., the multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores.
-
C.
Oxford Dictionaries American English corpus
The Oxford Dictionaries American English corpus is a large, curated collection of contemporary American English texts used to analyze usage and inform the content of Oxford’s American English dictionaries.
-
D.
Hugging Face Datasets
Hugging Face Datasets is an open-source library that provides a large collection of ready-to-use datasets and efficient data loading tools for machine learning and natural language processing workflows.
-
E.
WebText dataset
The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: WMT English-French dataset Target entity description: The WMT English-French dataset is a large-scale parallel corpus of English–French sentence pairs widely used as a benchmark for training and evaluating machine translation systems.
-
A.
CORDE corpus
The CORDE corpus is a large historical Spanish language corpus compiled by the Royal Spanish Academy, used for studying the evolution and usage of Spanish over time.
-
B.
WMT
WMT is the stock ticker symbol for Walmart Inc., the multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores.
-
C.
Oxford Dictionaries American English corpus
The Oxford Dictionaries American English corpus is a large, curated collection of contemporary American English texts used to analyze usage and inform the content of Oxford’s American English dictionaries.
-
D.
Hugging Face Datasets
Hugging Face Datasets is an open-source library that provides a large collection of ready-to-use datasets and efficient data loading tools for machine learning and natural language processing workflows.
-
E.
WebText dataset
The WebText dataset is a large-scale corpus of web pages curated by OpenAI to train language models like GPT-2 on diverse, high-quality internet text.
- F. None of above. chosen
Statements (47)
| Predicate | Object |
|---|---|
| instanceOf |
English–French corpus
ⓘ
bilingual dataset ⓘ machine translation benchmark ⓘ parallel corpus ⓘ |
| accessMethod | download from WMT or associated sites ⓘ |
| alignmentType | parallel sentences ⓘ |
| associatedEvent | Conference on Machine Translation NERFINISHED ⓘ |
| benchmarkFor |
neural machine translation systems
ⓘ
statistical machine translation systems ⓘ |
| benchmarkLevel | state-of-the-art comparison ⓘ |
| benchmarkStatus | standard benchmark in MT research ⓘ |
| contains | sentence pairs ⓘ |
| curatedBy | Workshop on Machine Translation organizers ⓘ |
| dataFormat |
plain text
ⓘ
tokenized text ⓘ |
| dataType | text ⓘ |
| domain | general-domain text ⓘ |
| evaluationMetric |
BLEU
NERFINISHED
ⓘ
COMET NERFINISHED ⓘ chrF ⓘ |
| evaluationSetting | shared task evaluation campaigns ⓘ |
| field |
machine translation
ⓘ
natural language processing ⓘ |
| granularity | sentence-level alignment ⓘ |
| includes |
development set
ⓘ
test set ⓘ training set ⓘ |
| languagePair | English–French NERFINISHED ⓘ |
| license | research use (varies by component corpus) ⓘ |
| modality | written language ⓘ |
| origin | crawled and curated web and text sources ⓘ |
| scale | large-scale ⓘ |
| sourceLanguage | English ⓘ |
| targetLanguage | French ⓘ |
| task | sentence-level translation ⓘ |
| timeSpan | updated annually in WMT campaigns ⓘ |
| typicalModel | sequence-to-sequence models ⓘ |
| typicalPreprocessing |
subword segmentation (e.g. BPE)
GENERATED
ⓘ
tokenization GENERATED ⓘ truecasing GENERATED ⓘ |
| typicalUse | supervised learning ⓘ |
| usedBy |
academic researchers
ⓘ
industry MT practitioners ⓘ |
| usedFor |
benchmarking translation quality
ⓘ
machine translation evaluation ⓘ machine translation training ⓘ |
| usedIn | WMT shared tasks ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: WMT English-French dataset Description of subject: The WMT English-French dataset is a large-scale parallel corpus of English–French sentence pairs widely used as a benchmark for training and evaluating machine translation systems.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.