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- W4382318477 abstract "Current sentence-level evidence extraction based methods may lose the discourse coherence of legal articles since they tend to make the extracted sentences scattered over the article. To solve the problem, this paper proposes a Cascaded Answer-guided key segment learning framework for long Legal article Question Answering, namely CALQA. The framework consists of three cascaded modules: Sifter, Reader, and Responder. The Sifter transfers a long legal article into several segments and works in an answer-guided way by automatically sifting out key fact segments in a coarse-to-fine approach through multiple iterations. The Reader utilizes a set of attention mechanisms to obtain semantic representations of the question and key fact segments. Finally, considering it a multi-label classification task the Responder predicts final answers in a cascaded manner. CALQA outperforms state-of-the-art methods in CAIL 2021 Law dataset." @default.
- W4382318477 created "2023-06-28" @default.
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- W4382318477 date "2023-06-26" @default.
- W4382318477 modified "2023-10-18" @default.
- W4382318477 title "Long Legal Article Question Answering via Cascaded Key Segment Learning (Student Abstract)" @default.
- W4382318477 doi "https://doi.org/10.1609/aaai.v37i13.27042" @default.
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