Papers with cross-lingual transferability
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | X-STA is a new approach for cross-lingual machine reading comprehension . the variation of answer span positions in different languages makes it difficult to transfer knowledge across languages. |
| Approach: | They propose a method that leverages an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on three multi-lingual datasets. |
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing (N19-1)
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| Challenge: | Existing studies on crosslingual transfer have focused on word-level information sharing, but words are not independent in sentences; their combinations form larger linguistic units, known as context. |
| Approach: | They propose to use orderagnostic models to transfer word order to distant languages . they train dependency parsers on an English corpus and evaluate their transfer performance on 30 other languages. |
| Outcome: | The proposed model performs better on languages with different word orders than on other languages. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent? (2025.findings-emnlp)
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| Challenge: | Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain factuality of model knowledge across languages, and preserve parity of language model performance. |
| Approach: | They examine pretrained and tuned models with code-mixed coreferential statements that convey identical knowledge across languages. |
| Outcome: | The proposed model shows different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and bottlenecks on a particular layer. |
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation (2021.acl-long)
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| Challenge: | Existing work in multilingual pretraining relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages. |
| Approach: | They propose to plug a cross-attention module into a Transformer encoder to explicitly build the interdependence between languages. |
| Outcome: | The proposed model outperforms existing models on XTREME and English-to-French translation datasets. |
On Negative Interference in Multilingual Models: Findings and A Meta-Learning Treatment (2020.emnlp-main)
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| Challenge: | Modern multilingual models are trained on concatenated text from multiple languages in hopes of conferring benefits to each (positive transfer) however, recent work has shown that this approach can degrade performance on high-resource languages, a phenomenon known as negative interference. |
| Approach: | They propose a meta-learning algorithm that adds language-specific parameters as meta-parameters and trains them in a manner that explicitly improves shared layers’ generalization on all languages. |
| Outcome: | The proposed model improves cross-lingual transferability and generalization on all languages, and improves on the language-specific parameters. |
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)
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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei
| Challenge: | ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive . |
| Approach: | They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora . |
| Outcome: | The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost. |
Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models (2025.emnlp-main)
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Mehdi Ali, Manuel Brack, Max Lübbering, Elias Wendt, Abbas Goher Khan, Richard Rutmann, Alex Jude, Maurice Kraus, Alexander Arno Weber, Felix Stollenwerk, David Kaczér, Florian Mai, Lucie Flek, Rafet Sifa, Nicolas Flores-Herr, Joachim Koehler, Patrick Schramowski, Michael Fromm, Kristian Kersting
| Challenge: | Existing open-source multilingual datasets rely on heuristic filtering methods restricting both their cross-lingual transferability and scalability. |
| Approach: | They propose a systematic approach that curates diverse and high-quality multilingual data at scale while significantly reducing computational demands. |
| Outcome: | Evaluated empirically across 35 languages, the proposed approach outperforms current heuristic filtering methods like Fineweb2 and improves model training quality and retention rates. |
Can Cross-Lingual Transferability of Multilingual Transformers Be Activated Without End-Task Data? (2023.findings-acl)
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| Challenge: | Existing methods for cross-lingual transfer learning cannot activate cross-linguistic transferability when end-task data are unavailable. |
| Approach: | They propose a cross-lingual transfer method that disassembles multilingual Transformers into sub-modules and reassembles them to be the multilingual end-task model. |
| Outcome: | The proposed method activates the cross-lingual transferability of multilingual Transformers without accessing end-task data. |
Debiasing Multilingual LLMs in Cross-lingual Latent Space (2025.emnlp-main)
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| Challenge: | Existing studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. |
| Approach: | They propose to perform debiasing in a joint latent space rather than directly on LLM representations by using an autoencoder trained on parallel TED talk scripts. |
| Outcome: | The proposed method improves both the overall debiasing performance and cross-lingual transferability of the proposed techniques across four languages. |
CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages (2026.findings-acl)
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| Challenge: | Existing methods for machine-generated text detection are mostly focused on English . existing methods are almost unusable for non-English languages, leaving the transferability towards these languages unexplored. |
| Approach: | They propose to use a train-language combination to compare MGT detection methods . they focus on multi-domain, multi-generator, and multilingual evaluation . |
| Outcome: | The proposed methods are the most performant in the Central European languages and resistant against obfuscation. |
Authorship Attribution in Multilingual Machine-Generated Texts (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have reached human-like fluency and coherence, but distinguishing machine-generated text from human-written content becomes increasingly difficult. |
| Approach: | They propose a problem of multilingual authorship attribution (AA) that involves attributing texts to human or multiple LLM generators across diverse languages. |
| Outcome: | The proposed method can be adapted to multilingual settings, but still has significant limitations and challenges. |