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• W3116461613 abstract "RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining. In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models. Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT. We further demonstrate that pretraining our parser on the recently available large-scale “silver-standard” discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis." @default.
• W3116461613 created "2021-01-05" @default.
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• W3116461613 date "2020-01-01" @default.
• W3116461613 modified "2023-09-23" @default.
• W3116461613 title "Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining" @default.
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• W3116461613 doi "https://doi.org/10.18653/v1/2020.coling-main.337" @default.
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