# Cross-lingual Transfer Learning for Fake News Detector in a Low-Resource Language

arXiv: https://arxiv.org/abs/2208.12482 · HTML: https://mlsanity.com/paper/2208.12482/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Unsupervised Alignment of Embeddings with Wasserstein Procrustes](https://mlsanity.com/paper/1805.11222/) (2018)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1606.01614/) (2016)
- [Trivial Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1809.00357/) (2018)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection](https://mlsanity.com/paper/2102.02680/) (2021)
- [Evaluating Language Model Finetuning Techniques for Low-resource Languages](https://mlsanity.com/paper/1907.00409/) (2019)
- [Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation](https://mlsanity.com/paper/1804.07878/) (2018)

## Abstract (arXiv)

Development of methods to detect fake news (FN) in low-resource languages has been impeded by a lack of training data. In this study, we solve the problem by using only training data from a high-resource language. Our FN-detection system permitted this strategy by applying adversarial learning that transfers the detection knowledge through languages. To assist the knowledge transfer, our system judges the reliability of articles by exploiting source information, which is a cross-lingual feature that represents the credibility of the speaker. In experiments, our system got 3.71% higher accuracy than a system that uses a machine-translated training dataset. In addition, our suggested cross-lingual feature exploitation for fake news detection improved accuracy by 3.03%.