# Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets

arXiv: https://arxiv.org/abs/2010.11574 · HTML: https://mlsanity.com/paper/2010.11574/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Improving Large-scale Language Models and Resources for Filipino](https://mlsanity.com/paper/2111.06053/) (2021)
- [Developing a Named Entity Recognition Dataset for Tagalog](https://mlsanity.com/paper/2311.07161/) (2023)
- [Batayan: A Filipino NLP benchmark for evaluating Large Language Models](https://mlsanity.com/paper/2502.14911/) (2025)
- [Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings](https://mlsanity.com/paper/2204.03251/) (2022)
- [calamanCy: A Tagalog Natural Language Processing Toolkit](https://mlsanity.com/paper/2311.07171/) (2023)
- [FilBench: Can LLMs Understand and Generate Filipino?](https://mlsanity.com/paper/2508.03523/) (2025)
- [Using Synthetic Data for Conversational Response Generation in Low-resource Settings](https://mlsanity.com/paper/2204.02653/) (2022)
- [Extracting General-use Transformers for Low-resource Languages via Knowledge Distillation](https://mlsanity.com/paper/2501.12660/) (2025)
- [Towards Stress-Aware Sentence-Level Filipino G2P With Weakly-Supervised ByT5 Fine-Tuning](https://mlsanity.com/paper/2609.09974/) (2026)
- [Echoes Across Vietnam's Highlands, Delta, and Coast: A Multilingual Corpus for Cham, Khmer, and Tay-Nung](https://mlsanity.com/paper/2607.08362/) (2026)

## Abstract (arXiv)

Transformers represent the state-of-the-art in Natural Language Processing (NLP) in recent years, proving effective even in tasks done in low-resource languages. While pretrained transformers for these languages can be made, it is challenging to measure their true performance and capacity due to the lack of hard benchmark datasets, as well as the difficulty and cost of producing them. In this paper, we present three contributions: First, we propose a methodology for automatically producing Natural Language Inference (NLI) benchmark datasets for low-resource languages using published news articles. Through this, we create and release NewsPH-NLI, the first sentence entailment benchmark dataset in the low-resource Filipino language. Second, we produce new pretrained transformers based on the ELECTRA technique to further alleviate the resource scarcity in Filipino, benchmarking them on our dataset against other commonly-used transfer learning techniques. Lastly, we perform analyses on transfer learning techniques to shed light on their true performance when operating in low-data domains through the use of degradation tests.