Papers by Benedikt Ebing
Kardeş-NLU: Transfer to Low-Resource Languages with Big Brother’s Help – A Benchmark and Evaluation for Turkic Languages (2024.eacl-long)
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| Challenge: | Cross-lingual transfer (XLT) driven by massively multilingual language models (mmLMs) has been shown to be ineffective for low-resource (LR) target languages with little (or no) representation in mmLM’s pretraining . |
| Approach: | They propose a benchmark to evaluate cross-lingual transfer (XLT) to LR languages that do have a close HR relative and a framework to integrate Turkish into XLT. |
| Outcome: | The proposed configuration is of practical relevance for more of the world’s languages: XLT to LR languages that do have a close HR relative. |
TransAlign: Machine Translation Encoders are Strong Word Aligners, Too (2025.findings-emnlp)
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| Challenge: | translation-based approaches to cross-lingual transfer (XLT) are limited. |
| Approach: | They propose a word aligner that utilizes the encoder of a massively multilingual MT model. |
| Outcome: | The proposed word aligner outperforms existing WA and state-of-the-art non-WA-based methods in token classification tasks. |
The Devil Is in the Word Alignment Details: On Translation-Based Cross-Lingual Transfer for Token Classification Tasks (2025.findings-acl)
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| Challenge: | Translation-based strategies for cross-lingual transfer XLT include label projection . word aligners (WAs) are commonly used for label projection, but low-level design decisions for using them have not been investigated . |
| Approach: | They revisit word aligners (WAs) for label projection and propose a new projection strategy that outperforms WAs. |
| Outcome: | The proposed projection strategy outperforms marker-based methods in token classification tasks. |
To Translate or Not to Translate: A Systematic Investigation of Translation-Based Cross-Lingual Transfer to Low-Resource Languages (2024.naacl-long)
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| Challenge: | XLT with multilingual language models is superfluous, says a new study . mBERT, XLM-R and mT5 are effective for cross-lingual transfer, authors say . |
| Approach: | They propose to use multilingual language models to improve cross-lingual transfer (XLT) they propose to add reliable translations to training data for XLT even for non-MT languages . |
| Outcome: | The proposed approaches outperform zero-shot XLT with mLMs, the authors show . the authors believe their findings warrant a broader inclusion of more robust translation-based baselines in XL research. |
One Script Instead of Hundreds? On Pretraining Romanized Encoder Language Models (2026.findings-acl)
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| Challenge: | a recent study has focused on setups that favor romanization for cross-lingual transfer . a fidelity-based approach is needed to improve performance for high-resource languages . |
| Approach: | They propose to pretrain LMs from scratch on romanized and original texts for six languages . they find that romanization improves encoding efficiency for segmental scripts at a negligible cost . |
| Outcome: | The proposed method reduces the loss of script-specific information and dilution of language-specific representations from increased subword overlap. |