# Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection

arXiv: https://arxiv.org/abs/2311.02025 · HTML: https://mlsanity.com/paper/2311.02025/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 67 references that are in the map.
- First posted on arXiv 3 Nov 2023.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [mGPT: Few-Shot Learners Go Multilingual](https://mlsanity.com/paper/2204.07580/) (2022)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness](https://mlsanity.com/paper/2009.10195/) (2020)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis](https://mlsanity.com/paper/2010.04543/) (2020)
- [A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters](https://mlsanity.com/paper/2012.15682/) (2020)
- [Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation](https://mlsanity.com/paper/1902.09884/) (2019)
- [Cross-lingual Zero- and Few-shot Hate Speech Detection Utilising Frozen Transformer Language Models and AXEL](https://mlsanity.com/paper/2004.13850/) (2020)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- [CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing](https://mlsanity.com/paper/2210.07074/) (2022)
- [Few-shot learning through contextual data augmentation](https://mlsanity.com/paper/2103.16911/) (2021)
- [Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection](https://mlsanity.com/paper/2206.00962/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Target-Aware Analysis of Data Augmentation for Hate Speech Detection](https://mlsanity.com/paper/2410.08053/) (2024)

## Abstract (arXiv)

Cross-lingual transfer learning from high-resource to medium and low-resource languages has shown encouraging results. However, the scarcity of resources in target languages remains a challenge. In this work, we resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection. For data augmentation, we analyze two existing techniques based on vicinal risk minimization and propose MIXAG, a novel data augmentation method which interpolates pairs of instances based on the angle of their representations. Our experiments involve seven languages typologically distinct from English and three different domains. The results reveal that the data augmentation strategies can enhance few-shot cross-lingual abusive language detection. Specifically, we observe that consistently in all target languages, MIXAG improves significantly in multidomain and multilingual environments. Finally, we show through an error analysis how the domain adaptation can favour the class of abusive texts (reducing false negatives), but at the same time, declines the precision of the abusive language detection model.