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Datasets:
GEM
/
opusparcus

Tasks:
Other
Languages:
German
English
Finnish
Tags:
paraphrasing
License:
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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

NB: This is the old format of the data set card. Generate a new one from the json in the repository!

Table of Contents

Dataset Description

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 "French, with 90% quality of the training data":

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")

Remark regarding the optimal choice of training set qualities: Previous work suggests that a larger and noisier set is better than a smaller and clean set. See Sjöblom et al. (2018). Paraphrase Detection on Noisy Subtitles in Six Languages. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, and Vahtola et al. (2021). Coping with Noisy Training Data Labels in Paraphrase Detection. In Proceedings of the 7th Workshop on Noisy User-generated Text.

Data Instances

As a concrete example, loading the English data requesting 95% quality of the train split produces the following:

>>> data = load_dataset("GEM/opusparcus", lang="en", quality=95)

>>> data
DatasetDict({
    test: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 982
    })
    validation: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1015
    })
    test.full: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1445
    })
    validation.full: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1455
    })
    train: Dataset({
        features: ['lang', 'sent1', 'sent2', 'annot_score', 'gem_id'],
        num_rows: 1000000
    })
})

>>> data["test"][0]
{'annot_score': 4.0, 'gem_id': 'gem-opusparcus-test-1587', 'lang': 'en', 'sent1': "I haven 't been contacted by anybody .", 'sent2': "Nobody 's contacted me ."}

>>> data["validation"][2]
{'annot_score': 3.0, 'gem_id': 'gem-opusparcus-validation-1586', 'lang': 'en', 'sent1': 'No promises , okay ?', 'sent2': "I 'm not promising anything ."}

>>> data["train"][1000]
{'annot_score': 0.0, 'gem_id': 'gem-opusparcus-train-12501001', 'lang': 'en', 'sent1': 'Am I beautiful ?', 'sent2': 'Am I pretty ?'}

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 Aulamo et al. (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, called 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 scores of the full sets thus range between 1.0 and 4.0. The regular validation and test sets only contain sentence pairs that qualify as 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

Opusparcus was created in order to produce a sentential paraphrase corpus for multiple languages containing colloquial language (as opposed to news or religious text, for instance).

Source Data

Initial Data Collection and Normalization

The data in Opusparcus has been extracted from OpenSubtitles2016, which is in turn based on data from http://www.opensubtitles.org/.

The sentences have been tokenized.

Who are the source language producers?

The texts consist of subtitles that have been produced using crowdsourcing.

Annotations

Annotation process

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.

The annot_score field reflects the judgments made by the annotators. If the annnotators fully agreed on the category (4.0: dark green, 3.0: light green, 2.0: yellow, 1.0: red), the value of annot_score is 4.0, 3.0, 2.0 or 1.0. If the 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.

Who are the annotators?

Students and staff at the University of Helsinki (native or very proficient speakers of the target languages)

Personal and Sensitive Information

The datasets do not contain any personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

The goal of Opusparcus is to promote the support for colloquial language.

Discussion of Biases

The data reflect the biases present in the movies and TV shows that have been subtitled.

Other Known Limitations

The sentence pairs in the validation and test sets have been selected in such a manner that their Levenshtein distance (minimum edit distance) exceeds a certain theshold. This guarantees that the manual annotation effort focuses on "interesting" sentence pairs rather than trivial variations (such as "It is good." vs. "It's good."). The training sets, however, have not been prefiltered in this manner and thus also contain highly similar sentences.

Additional Information

Dataset Curators

Mathias Creutz, University of Helsinki, Finland

Licensing Information

CC-BY-NC 4.0

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.