| # Dataset Card for register_oscar | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-instances) | |
| ### Dataset Summary | |
| The Register Oscar dataset is a multilingual dataset, containing languaegs from the Oscar dataset that have been tagged with register information. | |
| 8 main-level registers: | |
| * Narrative (NA) | |
| * Informational Description (IN) | |
| * Opinion (OP) | |
| * Interactive Discussion (ID) | |
| * How-to/Instruction (HI) | |
| * Informational Persuasion (IP) | |
| * Lyrical (LY) | |
| * Spoken (SP) | |
| For further description of the labels, see (Douglas Biber and Jesse Egbert. 2018. Register variation online) | |
| Code used to tag Register Oscar can be found at https://github.com/TurkuNLP/register-labeling | |
| ### Languages | |
| Currently contains the following languages: Arabic, Bengali, Catalan, English, Spanish, Basque, French, Hindi, Indonesian, Portuguese, Swahili, Urdu, Vietnamese and Chinese. | |
| For further information on the languages and data, see https://huggingface.co/datasets/oscar | |
| ## Dataset Structure | |
| ### Data Instances | |
| ``` | |
| {"id": "0", "labels": ["NA"], "text": "Zarif: Iran inajua mpango wa Saudia wa kufanya mauaji ya kigaidi dhidi ya maafisa wa ngazi za juu wa Iran\n"} | |
| ``` | |
| ### Data Fields | |
| * id: unique id of the document (from the Oscar dataset) | |
| * labels: the list of labels assigned to the text | |
| * text: the original text of the document (as appears in the Oscar dataset) | |
| ### Citing | |
| ``` | |
| @inproceedings{laippala-etal-2022-towards, | |
| title = "Towards better structured and less noisy Web data: Oscar with Register annotations", | |
| author = {Laippala, Veronika and | |
| Salmela, Anna and | |
| R{\"o}nnqvist, Samuel and | |
| Aji, Alham Fikri and | |
| Chang, Li-Hsin and | |
| Dhifallah, Asma and | |
| Goulart, Larissa and | |
| Kortelainen, Henna and | |
| P{\`a}mies, Marc and | |
| Prina Dutra, Deise and | |
| Skantsi, Valtteri and | |
| Sutawika, Lintang and | |
| Pyysalo, Sampo}, | |
| booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)", | |
| month = oct, | |
| year = "2022", | |
| address = "Gyeongju, Republic of Korea", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/2022.wnut-1.23", | |
| pages = "215--221", | |
| abstract = {Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by R{\"o}nnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at https://huggingface.co/datasets/TurkuNLP/register{\_}oscar.}, | |
| } | |
| ``` |