Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras, Ilias Kyparissidis, George Giannakoulas, Felix Gers, Alexander Loeser
| Challenge: | Current models for clinical phenotyping are limited to clinical notes written in English due to the large amount of labeled and unlabeled clinical text resources. |
| Approach: | They propose to use translation-based methods with domain-specific encoders and cross-lingual encoder plus adapters to perform this task for clinics that do not use the English language. |
| Outcome: | The proposed strategies outperform the state-of-the-art models for clinics that do not use the English language and have a small amount of in-domain data available. |
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| Challenge: | Recent advances in training multilingual models on large datasets have shown promising results in knowledge transfer across languages. |
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| Challenge: | a comprehensive survey of cutting-edge weakly-supervised and unsupervised cross-lingual word representations is presented . |
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| Challenge: | Existing studies have proposed data-based cross-lingual transfer as an effective technique for cross-linguistic sequence labelling, but they have failed to perform well. |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
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Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding (D19-1)
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