Papers with data translation

3 papers
Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)

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Challenge: Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages .
Approach: They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages.
Outcome: The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases.
Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniques (2024.acl-long)

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Challenge: Recent work on sequence labelling has explored different strategies to mitigate the lack of manually annotated data for the large majority of the world languages.
Approach: They propose to use the mask objective to exploit the few-shot capabilities of pre-trained language models to improve their performance.
Outcome: The proposed model-transfer outperforms data-transference and fine-tuning outperformed few-shot methods for Argument Mining task.
POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering (2026.acl-long)

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Challenge: Existing chart understanding benchmarks are overwhelmingly English-centric, limiting their accessibility and relevance to global audiences.
Approach: They propose a multilingual chart question answering benchmark that enables efficient multilingual generation via data translation and code reuse.
Outcome: The proposed benchmark systematically evaluates multilingual chart understanding on state-of-the-art LVLMs and shows a significant performance gap between English and other languages.

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