# Enhancing Cross-lingual Transfer by Manifold Mixup

arXiv: https://arxiv.org/abs/2205.04182 · HTML: https://mlsanity.com/paper/2205.04182/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 53 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 30 of 38)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training](https://mlsanity.com/paper/2007.07834/) (2020)
- [Unsupervised Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2005.10608/) (2020)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://mlsanity.com/paper/2004.12239/) (2020)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation](https://mlsanity.com/paper/2104.07412/) (2021)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks](https://mlsanity.com/paper/1909.00964/) (2019)
- [On the Language Neutrality of Pre-trained Multilingual Representations](https://mlsanity.com/paper/2004.05160/) (2020)
- [CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP](https://mlsanity.com/paper/2006.06402/) (2020)
- [Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing](https://mlsanity.com/paper/1807.00914/) (2018)
- [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://mlsanity.com/paper/2012.15674/) (2020)
- [On Learning Universal Representations Across Languages](https://mlsanity.com/paper/2007.15960/) (2020)
- [Explicit Alignment Objectives for Multilingual Bidirectional Encoders](https://mlsanity.com/paper/2010.07972/) (2020)
- [Inducing Language-Agnostic Multilingual Representations](https://mlsanity.com/paper/2008.09112/) (2020)

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

- [Understanding Cross-Lingual Alignment -- A Survey](https://mlsanity.com/paper/2404.06228/) (2024)
- [A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability](https://mlsanity.com/paper/2212.10888/) (2022)
- [English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings](https://mlsanity.com/paper/2211.06127/) (2022)
- [CMOT: Cross-modal Mixup via Optimal Transport for Speech Translation](https://mlsanity.com/paper/2305.14635/) (2023)
- [Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solution](https://mlsanity.com/paper/2305.17325/) (2023)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Language Representation Projection: Can We Transfer Factual Knowledge across Languages in Multilingual Language Models?](https://mlsanity.com/paper/2311.03788/) (2023)
- [Enhancing Cross-lingual Transfer via Phonemic Transcription Integration](https://mlsanity.com/paper/2307.04361/) (2023)
- [EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs](https://mlsanity.com/paper/2305.00664/) (2023)
- [To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages](https://mlsanity.com/paper/2311.09404/) (2023)
- [Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks](https://mlsanity.com/paper/2210.09588/) (2022)
- [Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer](https://mlsanity.com/paper/2402.14279/) (2024)
- [Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languages](https://mlsanity.com/paper/2411.02398/) (2024)
- [Analysis of Multi-Source Language Training in Cross-Lingual Transfer](https://mlsanity.com/paper/2402.13562/) (2024)
- [Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation](https://mlsanity.com/paper/2406.09003/) (2024)
- [XFEVER: Exploring Fact Verification across Languages](https://mlsanity.com/paper/2310.16278/) (2023)
- [CORI: CJKV Benchmark with Romanization Integration -- A step towards Cross-lingual Transfer Beyond Textual Scripts](https://mlsanity.com/paper/2404.12618/) (2024)
- [Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer](https://mlsanity.com/paper/2309.10891/) (2023)
- [SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe](https://mlsanity.com/paper/2410.05248/) (2024)
- [Language Fusion for Parameter-Efficient Cross-lingual Transfer](https://mlsanity.com/paper/2501.06892/) (2025)
- [ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation](https://mlsanity.com/paper/2402.12844/) (2024)
- [Translation Deserves Better: Analyzing Translation Artifacts in Cross-lingual Visual Question Answering](https://mlsanity.com/paper/2406.02331/) (2024)
- [Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models](https://mlsanity.com/paper/2308.08774/) (2023)
- [Rethinking Cross-Modal Fine-Tuning: Optimizing the Interaction Between Feature Alignment and Target Fitting](https://mlsanity.com/paper/2601.18231/) (2026)
- [ID-MixGCL: Identity Mixup for Graph Contrastive Learning](https://mlsanity.com/paper/2304.10045/) (2023)
- [Test-Time Adaptation with Perturbation Consistency Learning](https://mlsanity.com/paper/2304.12764/) (2023)
- [Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification](https://mlsanity.com/paper/2305.08800/) (2023)
- [Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension](https://mlsanity.com/paper/2311.06758/) (2023)
- [XITE: Cross-lingual Interpolation for Transfer using Embeddings](https://mlsanity.com/paper/2604.23589/) (2026)
- [Harnessing Linguistic Dissimilarity for Language Generalization on Unseen Low-Resource Varieties](https://mlsanity.com/paper/2605.04500/) (2026)

## Abstract (arXiv)

Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of target languages still lags far behind the source language. In this paper, our analyses indicate such a performance gap is strongly associated with the cross-lingual representation discrepancy. To achieve better cross-lingual transfer performance, we propose the cross-lingual manifold mixup (X-Mixup) method, which adaptively calibrates the representation discrepancy and gives a compromised representation for target languages. Experiments on the XTREME benchmark show X-Mixup achieves 1.8% performance gains on multiple text understanding tasks, compared with strong baselines, and significantly reduces the cross-lingual representation discrepancy.