annotations_creators:
- expert-generated
- machine-generated
language_creators:
- crowdsourced
languages:
- de
- en
- fi
- fr
- ru
- sv
licenses:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: Opusparcus
size_categories:
- unknown
source_datasets:
- extended|open_subtitles
task_categories:
- conditional-text-generation
task_ids:
- conditional-text-generation-other-paraphrase generation
Dataset Card for Opusparcus
Table of Contents
Dataset Description
Repository: Language Bank of Finland – Metashare
Paper: Mathias Creutz (2018): Open Subtitles Paraphrases Corpus For Six Languages
Point of Contact: Mathias Creutz (firstname dot lastname at helsinki dot fi)
Dataset Summary
Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases consist of subtitles from movies and TV shows.
The data in Opusparcus has been extracted from OpenSubtitles2016, which is in turn based on data from http://www.opensubtitles.org/.
For each target language, the Opusparcus data have been partitioned into three types of data sets: training, validation and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been annotated manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two indepedent annotators.
Supported Tasks and Leaderboards
Tasks: Paraphrase detection and generation
Leaderboards: Currently there is no Leaderboard for this dataset.
Languages
German (de), English (en), Finnish (fi), French (fr), Russian (ru), Swedish (sv)
Dataset Structure
When you download Opusparcus, you must always indicate the language you want to retrieve, for instance:
data = load_dataset("GEM/opusparcus", lang="de")
The above command will download the validation and test sets for German. If additionally, you want to retrieve training data, you need to specify the level of quality you desire, such as:
data = load_dataset("GEM/opusparcus", lang="fr", quality=90)
Data Instances
TBA
Data Fields
sent1: a tokenized sentence
sent2: another tokenized sentence, which is potentially a paraphrase of sent1.
annot_score: a value between 1.0 and 4.0 indicating how good an example of paraphrases sent1 and sent2 are. (For the training sets, the value is 0.0, which indicates that no manual annotation has taken place.)
lang: language of this dataset
gem_id: unique identifier of this entry
Additional information about the annotation scheme:
The annotation scores given by an individual annotator are:
4: Good example of paraphrases (Dark green button in the annotation tool): The two sentences can be used in the same situation and essentially "mean the same thing".
3: Mostly good example of paraphrases (Light green button in the annotation tool): It is acceptable to think that the two sentences refer to the same thing, although one sentence might be more specific than the other one, or there are differences in style, such as polite form versus familiar form.
2: Mostly bad example of paraphrases (Yellow button in the annotation tool): There is some connection between the sentences that explains why they occur together, but one would not really consider them to mean the same thing.
1: Bad example of paraphrases (Red button in the annotation tool): There is no obvious connection. The sentences mean different things.
If the two annotators fully agreed on the category, the value in the
annot_score field is 4.0, 3.0, 2.0 or 1.0. If the two annotators
chose adjacent categories, the value in this field will be 3.5, 2.5 or
1.5. For instance, a value of 2.5 means that one annotator gave a
score of 3 ("mostly good"), indicating a possible paraphrase pair,
whereas the other annotator scored this as a 2 ("mostly bad"), that
is, unlikely to be a paraphrase pair. If the annotators disagreed by
more than one category, the sentence pair was discarded and won't show
up in the datasets.
The training sets were not annotated manually. This is indicated by
the value 0.0 in the annot_score field.
For an assessment of of inter-annotator agreement, see Mikko Aulamo, Mathias Creutz and Eetu Sjöblom (2019). Annotation of subtitle paraphrases using a new web tool. In Proceedings of the Digital Humanities in the Nordic Countries 4th Conference], Copenhagen, Denmark.
Data Splits
The data is split into ...
| Train | Valid | Test | |
|---|---|---|---|
| de | ... | ... | ... |
| en | ... | ... | ... |
Dataset Creation
Curation Rationale
TBA
Source Data
Initial Data Collection and Normalization
TBA
Who are the source language producers?
TBA
Annotations
Annotation process
TBA
Who are the annotators?
TBA
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
TBA
Licensing Information
[More Information Needed]
Citation Information
@InProceedings{creutz:lrec2018,
title = {Open Subtitles Paraphrase Corpus for Six Languages},
author={Mathias Creutz},
booktitle={Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC 2018)},
year={2018},
month = {May 7-12},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english},
url={http://www.lrec-conf.org/proceedings/lrec2018/pdf/131.pdf}
Contributions
Thanks to @mathiascreutz for adding this dataset.