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About this paper
Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua
This survey provides a comprehensive review of Open-domain Question Answering (OpenQA), focusing on the transformative impact of deep learning and the prevalent 'Retriever-Reader' architecture. It categorizes current methodologies in retrieval and reading, highlights the shift to neural networks, and identifies remaining challenges like noisy distant supervision and retrieval efficiency.
Key insights
- The 'Retriever-Reader' architecture, integrating neural Machine Reading Comprehension, has become the dominant and most effective paradigm for modern Open-domain Question Answering systems.
- Deep neural networks have largely replaced traditional linguistic analysis across all stages of OpenQA, leading to advancements in dense retrieval, sophisticated neural readers, and end-to-end trainable systems.
- Despite progress, OpenQA still faces challenges related to distant supervision noise, the trade-off between retrieval effectiveness and efficiency, effective knowledge incorporation, and handling conversational contexts.
Summary by alphaXiv.
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