|
--- |
|
language: |
|
- ca |
|
- en |
|
multilinguality: |
|
- multilingual |
|
pretty_name: CA-EN Parallel Corpus |
|
size_categories: |
|
- 10M<n<100M |
|
task_categories: |
|
- translation |
|
task_ids: [] |
|
license: cc-by-4.0 |
|
--- |
|
|
|
# Dataset Card for CA-EN Parallel Corpus |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Splits](#data-instances) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Source Data](#source-data) |
|
- [Data preparation](#data-preparation) |
|
- [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) |
|
- [Author](#author) |
|
- [Contact Information](#contact-information) |
|
- [Copyright](#copyright) |
|
- [Licensing information](#licenciung-informatrion) |
|
- [Funding](#funding) |
|
|
|
## Dataset Description |
|
|
|
### Dataset Summary |
|
|
|
The CA-EN Parallel Corpus is a Catalan-English dataset of **14.385.296** parallel sentences. The dataset was created to support Catalan in NLP tasks, specifically |
|
Machine Translation. |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
The dataset can be used to train Bilingual Machine Translation models between English and Catalan in any direction, |
|
as well as Multilingual Machine Translation models. |
|
|
|
### Languages |
|
|
|
The sentences included in the dataset are in Catalan (CA) and English (EN). |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
The dataset is a single tsv file where each row contains a parallel sentence pair and additional domain and text type information for each sentence. |
|
Datafields are separated by \, |
|
Text delimiter is \" |
|
|
|
|
|
### Data Fields |
|
|
|
Each example contains the following fields: |
|
* sentence_id: unique alphanumeric sentence identifier |
|
* en: ENGLISH |
|
* en_sentence: English sentence |
|
* ca: CATALAN |
|
* ca_sentence: Catalan sentence |
|
* domain: sentence domain |
|
* text_type: sentence text type |
|
|
|
#### Example: |
|
|
|
<pre> |
|
[ |
|
{ |
|
"00000a47-e4a5-8ab6-e0fa-3cbbeb596f34","en","As for the search engines, they also rely on the structure of your information content on the website to analyze and index your website.","ca","Pel que fa als motors de cerca, també es basen en l'estructura del seu contingut d'informació al lloc web per analitzar i indexar el seu lloc web.","MWM","SM" |
|
|
|
}, |
|
... |
|
] |
|
|
|
</pre> |
|
|
|
####List of domains |
|
|
|
AUT: Automotive, transport, traffic regulations |
|
LEG: legal, law, HR, certificates, degrees |
|
MWM: Marketing, web, merchandising, customer support and service, e-commerce , advertising, surveys |
|
LSM: Medicine, natural sciences, food/nutrition, biology, sexology, cosmetics, chemistry, genetics |
|
ENV: Environment, agriculture, forestry, fisheries, farming, zoology, ecology |
|
FIN: Finance, economics, business, entrepreneurship, business, competitions, labour, employment, accounting, insurance, insurance |
|
POL: Politics, international relations, European Union, international organisations, defence, military |
|
PRN: Porn, inappropriate content |
|
COM: Computers, IT, robotics, domotics, home automation, telecommunications |
|
ING: Pure engineering (mechanical, electrical, electronic, aerospace...), meteorology, mining, engineering, maritime, acoustics |
|
ARC: Architecture, civil engineering, construction, public engineering |
|
MAT: Mathematics, statistics, physics |
|
HRM: History, religion, mythology, folklore, philosophy, psychology, ethics, anthropology, tourism |
|
CUL: Art, poetry, literature, cinema, video games, theatre, theatre/film scripts, esotericism, astrology, sports, music, photography |
|
GEN: General - generic cathegory with topics such as clothing, textiles, gastronomy, etc. |
|
|
|
|
|
####List of text types |
|
|
|
PAT: Patents |
|
SM: Social Media (social networks, chats, forums, tweets...) |
|
CON: Vernacular (transcription of conversations, subtitles) |
|
EML: Emails |
|
MNL: Manuals, data sheets |
|
NEW: News, journalism |
|
GEN: Prose, generic type of text |
|
|
|
|
|
### Data Splits |
|
|
|
The dataset contains a single split: `train`. |
|
|
|
## Dataset Creation |
|
|
|
### Curation Rationale |
|
|
|
This dataset is aimed at promoting the development of Machine Translation between Catalan and other languages, specifically English. |
|
|
|
### Source Data |
|
|
|
#### Initial Data Collection and Normalization |
|
|
|
The data is a brand new collection of parallel sentences in Catalan and English, partially derived from web crawlings and belonging to a mix of different |
|
domains and styles. |
|
The source data is Catalan authentic text translated to English or authentic English text translated to Catalan. |
|
|
|
The data was obtained through a combination of human translation and machine translation with human proofreading. |
|
After the translation process, the data was deduplicated and filtered to remove any sentence pairs with a cosine similarity of less than 0.75 in order |
|
to improve the data alignment quality. |
|
This was done using sentence embeddings calculated using [LaBSE](https://huggingface.co/sentence-transformers/LaBSE). |
|
The obtained cleaned corpus consists of **14.385.296** parallel sentences of human quality. |
|
|
|
#### Who are the source language producers? |
|
|
|
The original data gathering was entrusted to an external company through a public tender process. |
|
|
|
### Annotations |
|
|
|
#### Annotation process |
|
|
|
The dataset does not contain any annotations. |
|
|
|
#### Who are the annotators? |
|
|
|
[N/A] |
|
|
|
### Personal and Sensitive Information |
|
|
|
No anonymisation process was performed. |
|
|
|
## Considerations for Using the Data |
|
|
|
### Social Impact of Dataset |
|
|
|
By providing this resource, we intend to promote the use of Catalan across NLP tasks, thereby improving the accessibility and visibility of the Catalan language. |
|
|
|
### Discussion of Biases |
|
|
|
No specific bias mitigation strategies were applied to this dataset. |
|
Inherent biases may exist within the data. |
|
|
|
### Other Known Limitations |
|
|
|
The dataset contains data of several specific domains. Application of this dataset in other domains would be of limited use. |
|
|
|
## Additional Information |
|
|
|
### Dataset Curators |
|
|
|
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es). |
|
|
|
This work has been promoted and financed by the Generalitat de Catalunya through the [Aina project](https://projecteaina.cat/). |
|
|
|
|
|
### Licensing Information |
|
|
|
This work is licensed under a Creative Commons Attribution 4.0 International license(https://creativecommons.org/licenses/by/4.0/). |
|
|
|
### Citation Information |
|
|
|
[N/A] |
|
|
|
### Contributions |
|
|
|
[N/A] |
