مراجع:
أون دي
استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:
ds = tfds.load('huggingface:wmt20_mlqe_task2/en-de')
- وصف :
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.
Task 2 evaluates the application of QE for post-editing purposes. It consists of predicting:
- A/ Word-level tags. This is done both on source side (to detect which words caused errors)
and target side (to detect mistranslated or missing words).
- A1/ Each token is tagged as either `OK` or `BAD`. Additionally,
each gap between two words is tagged as `BAD` if one or more
missing words should have been there, and `OK` otherwise. Note
that number of tags for each target sentence is 2*N+1, where
N is the number of tokens in the sentence.
- A2/ Tokens are tagged as `OK` if they were correctly
translated, and `BAD` otherwise. Gaps are not tagged.
- B/ Sentence-level HTER scores. HTER (Human Translation Error Rate)
is the ratio between the number of edits (insertions/deletions/replacements)
needed and the reference translation length.
- الترخيص : غير معروف
- الإصدار : 1.1.0
- الإنشقاقات :
| ينقسم | أمثلة |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- سمات :
{
"translation": {
"languages": [
"en",
"de"
],
"id": null,
"_type": "Translation"
},
"src_tags": {
"feature": {
"num_classes": 2,
"names": [
"BAD",
"OK"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mt_tags": {
"feature": {
"num_classes": 2,
"names": [
"BAD",
"OK"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pe": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hter": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"alignments": {
"feature": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
أون ز
استخدم الأمر التالي لتحميل مجموعة البيانات هذه في TFDS:
ds = tfds.load('huggingface:wmt20_mlqe_task2/en-zh')
- وصف :
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.
Task 2 evaluates the application of QE for post-editing purposes. It consists of predicting:
- A/ Word-level tags. This is done both on source side (to detect which words caused errors)
and target side (to detect mistranslated or missing words).
- A1/ Each token is tagged as either `OK` or `BAD`. Additionally,
each gap between two words is tagged as `BAD` if one or more
missing words should have been there, and `OK` otherwise. Note
that number of tags for each target sentence is 2*N+1, where
N is the number of tokens in the sentence.
- A2/ Tokens are tagged as `OK` if they were correctly
translated, and `BAD` otherwise. Gaps are not tagged.
- B/ Sentence-level HTER scores. HTER (Human Translation Error Rate)
is the ratio between the number of edits (insertions/deletions/replacements)
needed and the reference translation length.
- الترخيص : غير معروف
- الإصدار : 1.1.0
- الإنشقاقات :
| ينقسم | أمثلة |
|---|---|
'test' | 1000 |
'train' | 7000 |
'validation' | 1000 |
- سمات :
{
"translation": {
"languages": [
"en",
"zh"
],
"id": null,
"_type": "Translation"
},
"src_tags": {
"feature": {
"num_classes": 2,
"names": [
"BAD",
"OK"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"mt_tags": {
"feature": {
"num_classes": 2,
"names": [
"BAD",
"OK"
],
"names_file": null,
"id": null,
"_type": "ClassLabel"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"pe": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"hter": {
"dtype": "float32",
"id": null,
"_type": "Value"
},
"alignments": {
"feature": {
"feature": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}