# UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP

arXiv: https://arxiv.org/abs/2004.13240 · HTML: https://mlsanity.com/paper/2004.13240/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER](https://mlsanity.com/paper/2108.13655/) (2021)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [Model and Data Transfer for Cross-Lingual Sequence Labelling in Zero-Resource Settings](https://mlsanity.com/paper/2210.12623/) (2022)
- [The Zeno's Paradox of `Low-Resource' Languages](https://mlsanity.com/paper/2410.20817/) (2024)
- [ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER](https://mlsanity.com/paper/2306.00928/) (2023)
- [Nearest Neighbour Few-Shot Learning for Cross-lingual Classification](https://mlsanity.com/paper/2109.02221/) (2021)
- [Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer](https://mlsanity.com/paper/2305.12761/) (2023)
- [DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.08104/) (2022)
- [Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations](https://mlsanity.com/paper/2211.08794/) (2022)
- [Consistent Text Categorization using Data Augmentation in e-Commerce](https://mlsanity.com/paper/2305.05402/) (2023)
- [Data Augmentation for Neural NLP](https://mlsanity.com/paper/2302.11412/) (2023)
- [Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages](https://mlsanity.com/paper/2305.14949/) (2023)
- [UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding](https://mlsanity.com/paper/2406.16372/) (2024)

## Abstract (arXiv)

Transfer learning has yielded state-of-the-art (SoTA) results in many supervised NLP tasks. However, annotated data for every target task in every target language is rare, especially for low-resource languages. We propose UXLA, a novel unsupervised data augmentation framework for zero-resource transfer learning scenarios. In particular, UXLA aims to solve cross-lingual adaptation problems from a source language task distribution to an unknown target language task distribution, assuming no training label in the target language. At its core, UXLA performs simultaneous self-training with data augmentation and unsupervised sample selection. To show its effectiveness, we conduct extensive experiments on three diverse zero-resource cross-lingual transfer tasks. UXLA achieves SoTA results in all the tasks, outperforming the baselines by a good margin. With an in-depth framework dissection, we demonstrate the cumulative contributions of different components to its success.