# XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering

arXiv: https://arxiv.org/abs/1905.11471 · HTML: https://mlsanity.com/paper/1905.11471/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 30 of 35)

- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [Knowing More About Questions Can Help: Improving Calibration in Question Answering](https://mlsanity.com/paper/2106.01494/) (2021)
- [MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER](https://mlsanity.com/paper/2108.13655/) (2021)
- [Consistency Regularization for Cross-Lingual Fine-Tuning](https://mlsanity.com/paper/2106.08226/) (2021)
- [Enhancing Cross-lingual Transfer by Manifold Mixup](https://mlsanity.com/paper/2205.04182/) (2022)
- [ALLSH: Active Learning Guided by Local Sensitivity and Hardness](https://mlsanity.com/paper/2205.04980/) (2022)
- [Adapting Multilingual Speech Representation Model for a New, Underresourced Language through Multilingual Fine-tuning and Continued Pretraining](https://mlsanity.com/paper/2301.07295/) (2023)
- [To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP](https://mlsanity.com/paper/2111.09618/) (2021)
- [From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension](https://mlsanity.com/paper/2112.04735/) (2021)
- [El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing](https://mlsanity.com/paper/2101.10524/) (2021)
- [Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling](https://mlsanity.com/paper/2204.05210/) (2022)
- [Improved Text Classification via Test-Time Augmentation](https://mlsanity.com/paper/2206.13607/) (2022)
- [Explaining Translationese: why are Neural Classifiers Better and what do they Learn?](https://mlsanity.com/paper/2210.13391/) (2022)
- [Measuring Spurious Correlation in Classification: 'Clever Hans' in Translationese](https://mlsanity.com/paper/2308.13170/) (2023)
- [Analysis of Multi-Source Language Training in Cross-Lingual Transfer](https://mlsanity.com/paper/2402.13562/) (2024)
- [Agent Smith: Teaching Question Answering to Jill Watson](https://mlsanity.com/paper/2112.13677/) (2021)
- [Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data](https://mlsanity.com/paper/2305.05295/) (2023)
- [Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?](https://mlsanity.com/paper/2406.12822/) (2024)
- [Data Augmentation Methods for Anaphoric Zero Pronouns](https://mlsanity.com/paper/2109.09825/) (2021)
- [Towards Debiasing Translation Artifacts](https://mlsanity.com/paper/2205.08001/) (2022)
- [Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models](https://mlsanity.com/paper/2109.14350/) (2021)
- [HIT-SCIR at MMNLU-22: Consistency Regularization for Multilingual Spoken Language Understanding](https://mlsanity.com/paper/2301.02010/) (2023)
- [A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors](https://mlsanity.com/paper/2311.15954/) (2023)
- [Linguistic Knowledge in Data Augmentation for Natural Language Processing: An Example on Chinese Question Matching](https://mlsanity.com/paper/2111.14709/) (2021)
- [Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization](https://mlsanity.com/paper/2204.13512/) (2022)
- [ZusammenQA: Data Augmentation with Specialized Models for Cross-lingual Open-retrieval Question Answering System](https://mlsanity.com/paper/2205.14981/) (2022)
- [Cross-Lingual Cross-Age Group Adaptation for Low-Resource Elderly Speech Emotion Recognition](https://mlsanity.com/paper/2306.14517/) (2023)
- [Promoting Generalized Cross-lingual Question Answering in Few-resource Scenarios via Self-knowledge Distillation](https://mlsanity.com/paper/2309.17134/) (2023)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)

## Abstract (arXiv)

While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in another language. XLDA enhances performance of all 14 tested languages of the cross-lingual natural language inference (XNLI) benchmark. With improvements of up to 4.8%, training with XLDA achieves state-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrast to, and performs markedly better than, a more naive approach that aggregates examples in various languages in a way that each example is solely in one language. On the SQuAD question answering task, we see that XLDA provides a 1.0% performance increase on the English evaluation set. Comprehensive experiments suggest that most languages are effective as cross-lingual augmentors, that XLDA is robust to a wide range of translation quality, and that XLDA is even more effective for randomly initialized models than for pretrained models.