README.md

---
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](#dataset-description)

  - [Dataset Summary](#dataset-summary)

  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)

  - [Languages](#languages)

- [Dataset Structure](#dataset-structure)

  - [Data Instances](#data-instances)

  - [Data Fields](#data-fields)

  - [Data Splits](#data-splits)

- [Dataset Creation](#dataset-creation)

  - [Curation Rationale](#curation-rationale)

  - [Source Data](#source-data)

  - [Annotations](#annotations)

  - [Personal and Sensitive Information](#personal-and-sensitive-information)

- [Considerations for Using the Data](#considerations-for-using-the-data)

  - [Social Impact of Dataset](#social-impact-of-dataset)

  - [Discussion of Biases](#discussion-of-biases)

  - [Other Known Limitations](#other-known-limitations)

- [Additional Information](#additional-information)

  - [Dataset Curators](#dataset-curators)

  - [Licensing Information](#licensing-information)

  - [Citation Information](#citation-information)

  - [Contributions](#contributions)

## Dataset Description

- **Repository:** [Language Bank of Finland – Metashare](http://urn.fi/urn:nbn:fi:lb-2018021221)

- **Paper:** [Mathias Creutz (2018): Open Subtitles Paraphrases Corpus For Six Languages](http://www.lrec-conf.org/proceedings/lrec2018/summaries/131.html)

- **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](http://opus.nlpl.eu/OpenSubtitles2016.php), 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](http://noisy-text.github.io/2018/pdf/W-NUT20189.pdf). 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](https://aclanthology.org/2021.wnut-1.32/). 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.](http://ceur-ws.org/Vol-2364/3_paper.pdf) 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](http://opus.nlpl.eu/OpenSubtitles2016.php), 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](https://github.com/mathiascreutz) for adding this dataset.