annotations_creators:
- no-annotation
language_creators:
- expert-generated
- found
language:
- en
- pl
- ru
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: Poleval2019Mt
dataset_info:
- config_name: ru-pl
features:
- name: translation
dtype:
translation:
languages:
- ru
- pl
splits:
- name: train
num_bytes: 2818015
num_examples: 20001
- name: validation
num_bytes: 415735
num_examples: 3001
- name: test
num_bytes: 266462
num_examples: 2969
download_size: 3355801
dataset_size: 3500212
- config_name: en-pl
features:
- name: translation
dtype:
translation:
languages:
- en
- pl
splits:
- name: train
num_bytes: 13217798
num_examples: 129255
- name: validation
num_bytes: 1209168
num_examples: 10001
- name: test
num_bytes: 562482
num_examples: 9845
download_size: 13851405
dataset_size: 14989448
- config_name: pl-ru
features:
- name: translation
dtype:
translation:
languages:
- pl
- ru
splits:
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num_bytes: 2818015
num_examples: 20001
- name: validation
num_bytes: 415735
num_examples: 3001
- name: test
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num_examples: 2967
download_size: 3355801
dataset_size: 3383173
- config_name: pl-en
features:
- name: translation
dtype:
translation:
languages:
- pl
- en
splits:
- name: train
num_bytes: 13217798
num_examples: 129255
- name: validation
num_bytes: 1209168
num_examples: 10001
- name: test
num_bytes: 16
num_examples: 1
download_size: 13591306
dataset_size: 14426982
Dataset Card for poleval2019_mt
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: PolEval-2019 competition. http://2019.poleval.pl/
- Repository: Links available in this page
- Paper:
- Leaderboard:
- Point of Contact:
Dataset Summary
PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted solutions compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures. One of the tasks in PolEval-2019 was Machine Translation (Task-4).
The task is to train as good as possible machine translation system, using any technology,with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions).
Here, Polish-English is also made available to allow for training in both directions. However, the test data is ONLY available for English-Polish
Supported Tasks and Leaderboards
Supports Machine Translation between Russian to Polish and English to Polish (and vice versa).
Languages
- Polish (pl)
- Russian (ru)
- English (en)
Dataset Structure
Data Instances
As the training data set, a set of bi-lingual corpora aligned at the sentence level has been prepared. The corpora are saved in UTF-8 encoding as plain text, one language per file.
Data Fields
One example of the translation is as below:
{
'translation': {'ru': 'не содержала в себе моделей. Модели это сравнительно новое явление. ',
'pl': 'nie miała w sobie modeli. Modele to względnie nowa dziedzina. Tak więc, jeśli '}
}
Data Splits
The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
| train | validation | test | |
|---|---|---|---|
| ru-pl | 20001 | 3001 | 2969 |
| pl-ru | 20001 | 3001 | 2969 |
| en-pl | 129255 | 1000 | 9845 |
Dataset Creation
Curation Rationale
This data was curated as a task for the PolEval-2019. The task is to train as good as possible machine translation system, using any technology, with limited textual resources. The competition will be done for 2 language pairs, more popular English-Polish (into Polish direction) and pair that can be called low resourced Russian-Polish (in both directions).
PolEval is a SemEval-inspired evaluation campaign for natural language processing tools for Polish. Submitted tools compete against one another within certain tasks selected by organizers, using available data and are evaluated according to pre-established procedures.
PolEval 2019-related papers were presented at AI & NLP Workshop Day (Warsaw, May 31, 2019). The links for the top performing models on various tasks (including the Task-4: Machine Translation) is present in this link
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
The organization details of PolEval is present in this link
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
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
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@proceedings{ogr:kob:19:poleval,
editor = {Maciej Ogrodniczuk and Łukasz Kobyliński},
title = {{Proceedings of the PolEval 2019 Workshop}},
year = {2019},
address = {Warsaw, Poland},
publisher = {Institute of Computer Science, Polish Academy of Sciences},
url = {http://2019.poleval.pl/files/poleval2019.pdf},
isbn = "978-83-63159-28-3"}
}
Contributions
Thanks to @vrindaprabhu for adding this dataset.