Papers with end-to-end multi-hop QA

1 papers
Weakly Supervised Pre-Training for Multi-Hop Retriever (2021.findings-acl)

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Challenge: Existing methods for weakly supervised multi-hop pretraining require costly human annotation.
Approach: They propose a method for weakly supervised multi-hop retriever pretraining without human efforts by generating vector representations of complex questions and subquestion as weak supervision for pre-training.
Outcome: The proposed method is effective and robust on limited data and computational resources.

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