Simple BERT Models for Relation Extraction and Semantic Role Labeling

Simplifying graph convolutional networks

Original

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Deep semantic role labeling: What works and what’s next

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A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling

Diego Marcheggiani, Anton Frolov, and Ivan Titov. 2017 · 2017

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Encoding sentences with graph convolutional networks for semantic role labeling

Diego Marcheggiani and Ivan Titov. 2017 · 2017

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Joint bi-affine parsing and semantic role labeling

Peng Shi and Yue Zhang. 2017 · 2017

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Position-aware attention and supervised data improve slot filling

Yuhao Zhang, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017 · 2017

A span selection model for semantic role labeling

Hiroki Ouchi, Hiroyuki Shindo, and Yuji Matsumoto. 2018 · 2018

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Deep contextualized word representations

Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018

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Linguistically-informed self-attention for semantic role labeling

Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018

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Graph convolution over pruned dependency trees improves relation extraction

Yuhao Zhang, Peng Qi, and Christopher D. Manning. 2018 · 2018

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Improving relation extraction by pre-trained language representations