Alexey Tikhonov, Alex Malkhasov, Andrey Manoshin, George-Andrei Dima, Réka Cserháti, Md.Sadek Hossain Asif, Matt Sárdi
| Challenge: | Existing NLP resources for Eastern European languages are sparse. |
| Approach: | They propose to use existing Eastern European language resources to build cross-lingual datasets for five different semantic tasks to support commonsense reasoning. |
| Outcome: | The proposed model trains on 104 languages and shows impressive results on text analysis tasks. |
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Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources (2022.findings-emnlp)
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| Challenge: | Existing studies have examined the quality of labeled data in non-English languages. |
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IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding (2020.aacl-main)
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Bryan Wilie, Karissa Vincentio, Genta Indra Winata, Samuel Cahyawijaya, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti
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Baselines and Test Data for Cross-Lingual Inference (L18-1)
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| Challenge: | Recent research on textual entailment is limited to English, but it is expanding to other languages. |
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IndoNLI: A Natural Language Inference Dataset for Indonesian (2021.emnlp-main)
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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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A Survey on Cross-Lingual Summarization (2022.tacl-1)
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| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
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