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BERT for Coreference Resolution: Baselines and Analysis

2019-08-24 · IJCNLP 2019 11 · Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available.

📄 PDF Abstract BibTeX arXiv:1908.09091

Code (2)

mandarjoshi90/coref 공식 구현 tf
wooseok-AI/Korean_e2e_CR_BERT tf

Tasks

Coreference Resolution

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
Residual Connection 설명 없음
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.
Weight Decay 설명 없음
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