Papers with retriever-reader model

2 papers
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (N19-1)

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Challenge: Existing approaches to open-domain question answering struggle to retrieve indirectly related evidence when no direct evidence is provided.
Approach: They propose a retriever-reader model that learns to attend on essential terms during the question answering process.
Outcome: The proposed model achieves the state-of-the-art on multiple open-domain QA datasets and achieves a 'reader-reader' level.
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)

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Challenge: a recent study aims to answer factual questions using a structured knowledge base (KBQA).
Approach: They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge.
Outcome: The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods.

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