Papers with multilingual pre-trained models

3 papers
Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models (2022.naacl-main)

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Challenge: Existing studies show that multilingual pre-trained models can learn to generalise across languages . however, it remains unclear how these models learn to learn multilingual representations .
Approach: They propose a hypothesis that multilingual pre-trained models can derive language-universal abstractions about grammar by aligning morphosyntactic markers that fulfil a similar grammatical function across languages.
Outcome: The proposed model can derive language-universal abstractions even without explicit supervision.
Probing Structured Pruning on Multilingual Pre-trained Models: Settings, Algorithms, and Efficiency (2022.acl-long)

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Challenge: Structured pruning has been extensively studied on monolingual pre-trained models . but little attention has been paid to evaluating the effectiveness of structured pruning on multilingual models.
Approach: They investigate settings, algorithms, and efficiency of structured pruning on multilingual models . authors propose a simple approach that allows training the model once and adapting to different model sizes at inference .
Outcome: The proposed approach allows training the model once and adapting to different model sizes at inference.
EXAMS: A Multi-subject High School Examinations Dataset for Cross-lingual and Multilingual Question Answering (2020.emnlp-main)

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Challenge: EXAMS is a benchmark dataset for cross-lingual and multilingual question answering for high school examinations.
Approach: They propose to use EXAMS to evaluate cross-lingual and multilingual question answering for high school examinations.
Outcome: The proposed model can be used to explore multilingual reasoning and knowledge transfer methods and pre-trained models in schools in different languages, which was not possible by now.

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