Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering (2020.acl-main)
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| Challenge: | Existing MCQA datasets are small in size, which increases difficulty of model learning and generalization. |
| Approach: | They propose a multi-source meta transfer framework for low-resource multiple-choice question answering . they extend meta learning by incorporating multiple training sources to learn a generalized feature representation across domains . |
| Outcome: | The proposed framework is independent of backbone language models and can bridge the distribution gap between training sources and target. |
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