Hybrid Hierarchical Retrieval for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Recent work shows that dense hierarchical retrieval (DHR) can outperform dense passage retrieval. |
| Approach: | They propose a framework that applies sparse, dense and a combination of them to document and passage retrieval. |
| Outcome: | The proposed framework can outperform dense hierarchical retrieval (DHR) and sparse retrievers (BM25) on open-domain question answering (ODQA) datasets with an average improvement of 4.69% on recall@100 over DHR. |
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| Challenge: | Recent work on open-domain question answering focuses on either extractive or generative readers exclusively. |
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| Challenge: | Large language models (LLMs) have recently pushed open-domain question answering (ODQA) to new heights. |
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| Challenge: | Existing approaches to document-based Opendomain Question Answering (ODQA) use flat text chunks or page-level images to locate the correct document. |
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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang
| Challenge: | Open-domain question answering uses dense passage retrieval to find answers . however, it is difficult to effectively train a dual-encoder due to discrepancy between training and inference . |
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| Challenge: | Existing generative models for open-domain question answering focus on generating direct answers from unstructured textual information, but a large amount of knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. |
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Efficient Passage Retrieval with Hashing for Open-domain Question Answering (2021.acl-short)
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| Challenge: | Open-domain question answering systems often require large memory to run because of the massive size of their passage index. |
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