indonlgSummarization

indonlg

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

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

Quick-Use

Contact Name

If known, provide the name of at least one person the reader can contact for questions about the dataset.

Genta Indra Winata

Multilingual?

Is the dataset multilingual?

yes

Covered Languages

What languages/dialects are covered in the dataset?

Indonesian, Javanese, Sundanese

License

What is the license of the dataset?

mit: MIT License

Communicative Goal

Provide a short description of the communicative goal of a model trained for this task on this dataset.

Generate a response according to the context and text.

Additional Annotations?

Does the dataset have additional annotations for each instance?

none

Contains PII?

Does the source language data likely contain Personal Identifying Information about the data creators or subjects?

unlikely

Dataset Overview
  • Where to find the Data and its Documentation

  • Languages and Intended Use

  • Credit

  • Dataset Structure

Where to find the Data and its Documentation

Webpage

What is the webpage for the dataset (if it exists)?

Github

Download

What is the link to where the original dataset is hosted?

Github

Paper

What is the link to the paper describing the dataset (open access preferred)?

ACL Anthology

BibTex

Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created 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

If known, provide the name of at least one person the reader can contact for questions about the dataset.

Genta Indra Winata

Contact Email

If known, provide the email of at least one person the reader can contact for questions about the dataset.

gentaindrawinata@gmail.com

Has a Leaderboard?

Does the dataset have an active leaderboard?

no

Languages and Intended Use

Multilingual?

Is the dataset multilingual?

yes

Covered Languages

What languages/dialects are covered in the dataset?

Indonesian, Javanese, Sundanese

License

What is the license of the dataset?

mit: MIT License

Intended Use

What is the intended use of the dataset?

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

Primary Task

What primary task does the dataset support?

Summarization

Communicative Goal

Provide a short description of the communicative goal of a model trained for this task on this dataset.

Generate a response according to the context and text.

Credit

Curation Organization Type(s)

In what kind of organization did the dataset curation happen?

academic, industry

Curation Organization(s)

Name the organization(s).

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

Dataset Creators

Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s).

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

Who funded the data creation?

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

Who added the Dataset to GEM?

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.

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

Dataset Structure

Dataset in GEM
  • Rationale for Inclusion in GEM

  • GEM-Specific Curation

  • Getting Started with the Task

Rationale for Inclusion in GEM

Similar Datasets

Do other datasets for the high level task exist?

yes

Unique Language Coverage

Does this dataset cover other languages than other datasets for the same task?

no

GEM-Specific Curation

Modificatied for GEM?

Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data?

yes

GEM Modifications

What changes have been made to he original dataset?

other

Additional Splits?

Does GEM provide additional splits to the dataset?

no

Getting Started with the Task

Previous Results
  • Previous Results

Previous Results

Measured Model Abilities

What aspect of model ability can be measured with this dataset?

Dialog understanding, summarization, translation

Metrics

What metrics are typically used for this task?

BLEU

Proposed Evaluation

List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task.

BLEU evaluates the generation quality.

Previous results available?

Are previous results available?

yes

Other Evaluation Approaches

What evaluation approaches have others used?

BLEU

Dataset Curation
  • Original Curation

  • Language Data

  • Structured Annotations

  • Consent

  • Private Identifying Information (PII)

  • Maintenance

Original Curation

Sourced from Different Sources

Is the dataset aggregated from different data sources?

no

Language Data

How was Language Data Obtained?

How was the language data obtained?

Crowdsourced

Where was it crowdsourced?

If crowdsourced, where from?

Participatory experiment

Data Validation

Was the text validated by a different worker or a data curator?

validated by data curator

Was Data Filtered?

Were text instances selected or filtered?

not filtered

Structured Annotations

Additional Annotations?

Does the dataset have additional annotations for each instance?

none

Annotation Service?

Was an annotation service used?

no

Consent

Any Consent Policy?

Was there a consent policy involved when gathering the data?

yes

Consent Policy Details

What was the consent policy?

Annotators agree using the dataset for research purpose.

Other Consented Downstream Use

What other downstream uses of the data did the original data creators and the data curators consent to?

Any

Private Identifying Information (PII)

Contains PII?

Does the source language data likely contain Personal Identifying Information about the data creators or subjects?

unlikely

Categories of PII

What categories of PII are present or suspected in the data?

``

Maintenance

Any Maintenance Plan?

Does the original dataset have a maintenance plan?

no

Broader Social Context
  • Previous Work on the Social Impact of the Dataset

  • Impact on Under-Served Communities

  • Discussion of Biases

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data

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

Addresses needs of underserved 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).

yes

Discussion of Biases

Any Documented Social 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

  • Licenses

  • Known Technical Limitations

PII Risks and Liability

Potential PII Risk

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.

No

Licenses

Copyright Restrictions on the Dataset

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

Copyright Restrictions on the Language Data

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

Known Technical Limitations