# Utilizing Weak Supervision To Generate Indonesian Conservation Dataset

arXiv: https://arxiv.org/abs/2310.11258 · HTML: https://mlsanity.com/paper/2310.11258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [WRENCH: A Comprehensive Benchmark for Weak Supervision](https://mlsanity.com/paper/2109.11377/) (2021)
- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Denoising Multi-Source Weak Supervision for Neural Text Classification](https://mlsanity.com/paper/2010.04582/) (2020)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling](https://mlsanity.com/paper/1910.04289/) (2019)
- [skweak: Weak Supervision Made Easy for NLP](https://mlsanity.com/paper/2104.09683/) (2021)
- [Multi-class Text Classification using BERT-based Active Learning](https://mlsanity.com/paper/2104.14289/) (2021)
- [ORCAS-I: Queries Annotated with Intent using Weak Supervision](https://mlsanity.com/paper/2205.00926/) (2022)
- [Word-Class Embeddings for Multiclass Text Classification](https://mlsanity.com/paper/1911.11506/) (2019)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [TweetDIS: A Large Twitter Dataset for Natural Disasters Built using Weak Supervision](https://mlsanity.com/paper/2207.04947/) (2022)

## Abstract (arXiv)

Weak supervision has emerged as a promising approach for rapid and large-scale dataset creation in response to the increasing demand for accelerated NLP development. By leveraging labeling functions, weak supervision allows practitioners to generate datasets quickly by creating learned label models that produce soft-labeled datasets. This paper aims to show how such an approach can be utilized to build an Indonesian NLP dataset from conservation news text. We construct two types of datasets: multi-class classification and sentiment classification. We then provide baseline experiments using various pretrained language models. These baseline results demonstrate test performances of 59.79% accuracy and 55.72% F1-score for sentiment classification, 66.87% F1-score-macro, 71.5% F1-score-micro, and 83.67% ROC-AUC for multi-class classification. Additionally, we release the datasets and labeling functions used in this work for further research and exploration.