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Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource Languages

Domain:

natural language processing

Record type:

paper
Creator:
PanArdBha
Host:avatar
Transformer based architectures have shown notable results on many down streaming tasks including question answering. The availability of data, on the other hand, impedes obtaining legitimate performance for low-resource languages. In this paper, we investigate the applicability of pre-trained multilingual models to improve the performance of question answering in low-resource languages. We tested four combinations of language and task adapters using multilingual transformer architectures on seven languages similar to MLQA dataset. Additionally, we have also proposed zero-shot transfer learning of low-resource question answering using language and task adapters. We observed that stacking the language and the task adapters improves the multilingual transformer models' performance significantly for low-resource languages.

Visit

arxiv.org

Tasks

question answeringtransfer learning

Tags

Computation and LanguageArtificial IntelligenceHuman-Computer InteractionInformation RetrievalMachine Learning

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