metadata

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

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

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 "90% quality of French":

data = load_dataset("GEM/opusparcus", lang="fr", quality=90)

The entries in the training sets have been ranked automatically by how likely they are paraphrases, best first, worst last. The quality parameter indicates the estimated proportion (in percent) of true paraphrases in the training set. Allowed quality values range between 60 and 100, in increments of 5 (60, 65, 70, ..., 100). A value of 60 means that 60% of the sentence pairs in the training set are estimated to be true paraphrases (and the remaining 40% are not). A higher value produces a smaller but cleaner set. The smaller sets are subsets of the larger sets, such that the quality=95 set is a subset of quality=90, which is a subset of quality=85, and so on.

The default quality value, if omitted, is 100. This matches no training data at all, which can be convenient, if you are only interested in the validation and test sets, which are considerably smaller, but manually annotated.

Note that an alternative to typing the parameter values explicitly, you can use configuration names instead. The following commands are equivalent to the ones above:

data = load_dataset("GEM/opusparcus", "de.100")
data = load_dataset("GEM/opusparcus", "fr.90")

TODO: Add comment about larger and noisier sets being better for training.

Data Instances

DatasetDict({
    test: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1047
    })
    validation: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1013
    })
    test.full: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1586
    })
    validation.full: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1582
    })
    train: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 590000
    })
})

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 training, validation and test sets. The validation and test sets come in two versions, the regular validation and test sets and the full sets validation.full and test.full. The full sets contain all sentence pairs successfully annotated by the annotators, including the sentence pairs that were rejected as paraphrases. The annotation score of the full sets thus range between 1.0 and 4.0. The regular validation and test sets only contain paraphrases, scored between 3.0 and 4.0 by the annotators.

The number of sentence pairs in the data splits are as follows for each of the languages. The range between the smallest (quality=95) and largest (quality=60) train configuration have been shown.

	train	valid	test	valid.full	test.full
de	0.59M .. 13M	1013	1047	1582	1586
en	1.0M .. 35M	1015	982	1455	1445
fi	0.48M .. 8.9M	963	958	1760	1749
fr	0.94M .. 22M	997	1007	1630	1674
ru	0.15M .. 15M	1020	1068	1854	1855
sv	0.24M .. 4.5M	984	947	1887	1901

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.

Datasets:

GEM
/

opusparcus

Dataset Card for Opusparcus

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions