Datasets:
GEM
/
indonlg

Tasks: unknown
Task Categories: summarization
Multilinguality: unknown
Size Categories: unknown
Language Creators: unknown
Annotations Creators: none
Source Datasets: original

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Dataset Card for GEM/indonlg

Link to Main Data Card

You can find the main data card on the GEM Website.

Dataset Summary

IndoNLG is a collection of various Indonesian, Javanese, and Sundanese NLG tasks including summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.

You can load the dataset via:

import datasets
data = datasets.load_dataset('GEM/indonlg')

The data loader can be found here.

website

Github

paper

ACL Anthology

authors

Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung

Dataset Overview

Where to find the Data and its Documentation

Webpage

Github

Download

Github

Paper

ACL Anthology

BibTex

@inproceedings{cahyawijaya-etal-2021-indonlg, title =  '{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation ', author =  'Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu and Purwarianti, Ayu and Fung, Pascale ', booktitle =  'Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ', month = nov, year =  '2021 ', address =  'Online and Punta Cana, Dominican Republic ', publisher =  'Association for Computational Linguistics ', url =  'https://aclanthology.org/2021.emnlp-main.699 ', pages =  '8875--8898 ', abstract =  'Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese. ',}

Contact Name

Genta Indra Winata

Contact Email

gentaindrawinata@gmail.com

Has a Leaderboard?

no

Languages and Intended Use

Multilingual?

yes

Covered Languages

Indonesian, Javanese, Sundanese

License

mit: MIT License

Intended Use

IndoNLG is a collection of Natural Language Generation (NLG) resources for Bahasa Indonesia with 10 downstream tasks.

Primary Task

Summarization

Communicative Goal

Generate a response according to the context and text.

Credit

Curation Organization Type(s)

academic, industry

Curation Organization(s)

The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai

Dataset Creators

Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung

Funding

The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai

Who added the Dataset to GEM?

Genta Indra Winata (The Hong Kong University of Science and Technology)

Dataset Structure

Dataset in GEM

Rationale for Inclusion in GEM

Similar Datasets

yes

Unique Language Coverage

no

GEM-Specific Curation

Modificatied for GEM?

yes

GEM Modifications

other

Additional Splits?

no

Getting Started with the Task

Previous Results

Previous Results

Measured Model Abilities

Dialog understanding, summarization, translation

Metrics

BLEU

Proposed Evaluation

BLEU evaluates the generation quality.

Previous results available?

yes

Other Evaluation Approaches

BLEU

Dataset Curation

Original Curation

Sourced from Different Sources

no

Language Data

How was Language Data Obtained?

Crowdsourced

Where was it crowdsourced?

Participatory experiment

Data Validation

validated by data curator

Was Data Filtered?

not filtered

Structured Annotations

Additional Annotations?

none

Annotation Service?

no

Consent

Any Consent Policy?

yes

Consent Policy Details

Annotators agree using the dataset for research purpose.

Other Consented Downstream Use

Any

Private Identifying Information (PII)

Contains PII?

unlikely

Categories of PII

``

Maintenance

Any Maintenance Plan?

no

Broader Social Context

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data

no

Impact on Under-Served Communities

Addresses needs of underserved Communities?

yes

Discussion of Biases

Any Documented Social Biases?

no

Considerations for Using the Data

PII Risks and Liability

Potential PII Risk

No

Licenses

Copyright Restrictions on the Dataset

open license

Copyright Restrictions on the Language Data

open license

Known Technical Limitations

Edit dataset card
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