Papers with textual question answering

2 papers
Robust Question Answering Through Sub-part Alignment (2021.naacl-main)

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Challenge: Current textual question answering models fail to generalize to out-of-domain settings.
Approach: They propose to decompose question and context into smaller units and align them to find the answer.
Outcome: The proposed model is more robust than the standard BERT QA model on adversarial and out-of-domain datasets.
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)

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Challenge: Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations .
Approach: They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering.
Outcome: The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning.

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