Datasets:
Dataset Overview
Where to find the data and its documentation
What is the webpage for the dataset (if it exists)?
https://turkunlp.org/paraphrase.html
What is the link to where the original dataset is hosted?
https://github.com/TurkuNLP/Turku-paraphrase-corpus
What is the link to the paper describing the dataset (open access preferred)?
https://aclanthology.org/2021.nodalida-main.29/
Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex.
@inproceedings{kanerva-etal-2021-finnish,
title = {Finnish Paraphrase Corpus},
author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpel{\"a}inen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sev{\'o}n, Maija and Tarkka, Otto},
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa'21)},
year = {2021},
publisher = {Link{\"o}ping University Electronic Press, Sweden},
url = {https://aclanthology.org/2021.nodalida-main.29},
pages = {288--298}
}
@inproceedings{kanerva-etal-2021-finnish, title = {Finnish Paraphrase Corpus}, author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpel{"a}inen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sev{'o}n, Maija and Tarkka, Otto}, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa'21)}, year = {2021}, publisher = {Link{"o}ping University Electronic Press, Sweden}, url = {https://aclanthology.org/2021.nodalida-main.29}, pages = {288--298} }
If known, provide the name of at least one person the reader can contact for questions about the dataset.
Jenna Kanerva, Filip Ginter
If known, provide the email of at least one person the reader can contact for questions about the dataset.
Does the dataset have an active leaderboard?
no
Languages and Intended Use
Is the dataset multilingual?
no
What dialects are covered? Are there multiple dialects per language?
written standard language, spoken language
What languages/dialects are covered in the dataset?
Finnish
What is the license of the dataset?
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
What is the intended use of the dataset?
Paraphrase classification, paraphrase generation
What primary task does the dataset support?
Paraphrasing
Provide a short description of the communicative goal of a model trained for this task on this dataset.
The corpus provides naturally occurring Finnish paraphrases striving for low lexical overlap, thus supporting many different downstream applications requiring language understanding.
Credit
In what kind of organization did the dataset curation happen?
academic
Name the organization(s).
University of Turku
Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s).
Jenna Kanerva, Filip Ginter, Li-Hsin Chang, Iiro Rastas, Valtteri Skantsi, Jemina Kilpeläinen, Hanna-Mari Kupari, Aurora Piirto, Jenna Saarni, Maija Sevón, Otto Tarkka (TurkuNLP / University of Turku)
Who funded the data creation?
The Turku paraphrase corpus project was funded by the Academy of Finland, as well as the European Language Grid project through its open call for pilot projects. The European Language Grid project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 825627 (ELG).
Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM.
Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)
Structure
List and describe the fields present in the dataset.
The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example include two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata.
The dataset include three different modes, plain, classification, and generation. The plain mode loads the original data without any additional preprocessing or transformations, while the classification mode directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the generation mode, the examples are preprocessed to be directly suitable for paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
Each pair in plain and classification mode will include fields:
gem_id: Identifier of the paraphrase pair (string)
goeswith: Identifier of the document from which the paraphrase was extracted, can be not available in case the source of the paraphrase is not from document-structured data (string)
fold: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)
text1: First paraphrase passage (string)
text2: Second paraphrase passage (string)
label: Manually annotated labels (string)
binary_label: Label turned into binary with values positive (paraphrase) and negative (not-paraphrase) (string)
is_rewrite: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)
Each pair in generation mode will include the same fields expect text1 and text2 are renamed to input and output in order to indicate the generation direction. Thus the fields are:
gem_id: Identifier of the paraphrase pair (string)
goeswith: Identifier of the document from which the paraphrase was extracted, can be not available in case the source of the paraphrase is not from document-structured data (string)
fold: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)
input: The input paraphrase passage for generation (string)
output: The output paraphrase passage for generation (string)
label: Manually annotated labels (string)
binary_label: Label turned into binary with values positive (paraphrase) and negative (not-paraphrase) (string)
is_rewrite: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)
Provide a JSON formatted example of a typical instance in the dataset.
{
'gem_id': 'gem-turku_paraphrase_corpus-train-15',
'goeswith': 'episode-02243',
'fold': 0,
'text1': 'Mitä merkitystä sillä on?',
'text2': 'Mitä väliä sillä edes on?',
'label': '4',
'binary_label': 'positive',
'is_rewrite': False
}
{ 'gem_id': 'gem-turku_paraphrase_corpus-train-15', 'goeswith': 'episode-02243', 'fold': 0, 'text1': 'Mitä merkitystä sillä on?', 'text2': 'Mitä väliä sillä edes on?', 'label': '4', 'binary_label': 'positive', 'is_rewrite': False }
Describe and name the splits in the dataset if there are more than one.
The corpus include 3 splits: train, validation, and test.
Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
The data is split randomly into the three section with a restriction of all paraphrases from the same document (movie, TV episode, news article, student translation, or exam question) being in the same section. All splits are manually annotated.
Dataset in GEM
Rationale
What does this dataset contribute toward better generation evaluation and why is it part of GEM?
This dataset provides a large amount of high quality (manually collected and verified) paraphrases for Finnish.
Do other datasets for the high level task exist?
yes
Does this dataset cover other languages than other datasets for the same task?
no
What aspect of model ability can be measured with this dataset?
natural language understanding, language variation
GEM Additional Curation
Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data?
yes
What changes have been made to he original dataset?
data points modified
For each of these changes, described them in more details and provided the intended purpose of the modification
Data structure is slightly simplified, and the release provides ready made transformations into two tasks (paraphrase classification and generation), where some data instances are doubled with different direction, and some are discarded as not being suitable for generation (e.g. negatives).
Does GEM provide additional splits to the dataset?
no
Getting Started
Previous Results
Previous Results
What aspect of model ability can be measured with this dataset?
natural language understanding, language variation
Are previous results available?
yes
What evaluation approaches have others used?
F-score in paraphrase classification
Dataset Curation
Original Curation
Original curation rationale
The dataset is fully manually annotated. The dataset strives for interesting paraphrases with low lexical overlap, thus the annotation is two fold. First the paraphrases are manually extracted from two related documents, where the annotators are instructed to extract only interesting paraphrases. In the second phrase, all extracted paraphrases are manually labeled given the annotation scheme.
The annotation scheme is: 4 : paraphrase in all reasonably possible contexts 3 : paraphrase in the given document contexts, but not in general 2 : related but not paraphrase During annotation also labels 1 (unrelated) and x (skip, e.g. wrong language) were used, however, the insignificant amount of examples annotated with these labels were discarded from the released corpus.
The following flags are annotated to label 4 paraphrases: < : txt1 is more general than txt2; txt2 is more specific than txt1 (directional paraphrase where txt2 can be replaced with txt1 in all contexts but not to the other direction)
: txt2 is more general than txt1; txt1 is more specific than txt2 (directional paraphrase where txt1 can be replaced with txt2 in all contexts but not to the other direction) i : minor traceable difference (differing in terms of grammatical number or case, 'this' vs 'that', etc.) s : style or strength difference (e.g. equivalent meaning, but one of the statements substantially more colloquial than the other)
For paraphrases where the annotated label was something else than label 4 without any flags, the annotators had an option to rewrite the text passages so that the rewritten paraphrase pair formed label 4 (universal) paraphrase. This was used for cases where simple edit would turn e.g. contextual or directional paraphrase into universal one. For the rewritten examples, both the original and the rewritten pairs are available with corresponding labels annotated.
What was the communicative goal?
Representing text passages with identical meaning but different surface realization.
Is the dataset aggregated from different data sources?
yes
List the sources (one per line)
movie and TV series subtitles (82%) news articles (9%) discussion forum messages (8%) university translation exercises (1%) university course essays and exams (<1%)
Language Data
How was the language data obtained?
Found Other
If found, where from?
Multiple websites Offline media collection Other
What further information do we have on the language producers?
The movie and TV series subtitles are extracted from OPUS OpenSubtitles2018 collection, which is based on data from http://www.opensubtitles.org/. The news articles are collected from two Finnish news sites, YLE and HS, during years 2017-2020. Discussion forum messages are obtained from the Finnish Suomi24 discussion forum released for academic use (http://urn.fi/urn:nbn:fi:lb-2020021801). University translation exercises, essays and exams are collected during the project.
Was the text validated by a different worker or a data curator?
validated by data curator
Were text instances selected or filtered?
not filtered
Structured Annotations
Does the dataset have additional annotations for each instance?
expert created
What is the number of raters
2<n<10
Describe the qualifications required of an annotator.
Members of the TurkuNLP research group, native speakers of Finnish, each annotator has a strong background in language studies by having an academic degree or ongoing studies in a field related to languages or linguistics.
How many annotators saw each training example?
1
How many annotators saw each test example?
1
Was an annotation service used?
no
Purpose and values for each annoation
- Manual extraction of interesting paraphrases from two related documents.
- Manual labeling of each extracted paraphrase based on the given annotation scheme, e.g. distinguishing contextual and universal paraphrases, marking style or strength differences, etc.
Quality control measures?
validated by another rater
Describe the quality control measures that were taken.
Partial double annotation, double annotation batches are assigned regularly in order to monitor annotation consistency. In double annotation, one annotator first extracts the candidate paraphrases, and these candidates are assigned to two different annotators, who does the label annotation independently from each other. Afterwards, the label annotations are merged, and conflicting labels are resolved together with the whole annotation team.
Consent
Was there a consent policy involved when gathering the data?
yes
What was the consent policy?
The corpus is mostly based on public/open data. For other data sources (student material), the licensing was agreed with the data providers during the collection.
Private Identifying Information (PII)
Does the source language data likely contain Personal Identifying Information about the data creators or subjects?
likely
What categories of PII are present or suspected in the data?
generic PII
Did the curators use any automatic/manual method to identify PII in the dataset?
no identification
Maintenance
Does the original dataset have a maintenance plan?
no
Broader Social Context
Previous Work on the Social Impact of the Dataset
Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems?
no
Impact on Under-Served Communities
Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models).
no
Discussion of Biases
Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group.
no
Considerations for Using the Data
PII Risks and Liability
Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset.
None
Licenses
Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset?
open license - commercial use allowed
Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data?
open license - commercial use allowed