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Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Daniel Fried
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Mitchell Stern
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Dan Klein
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Paper Details:
Month: July
Year: 2017
Location: Vancouver, Canada
Venue:
ACL |
Citations
URL
Two Local Models for Neural Constituent Parsing
Zhiyang Teng
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Yue Zhang
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Effective Inference for Generative Neural Parsing
Mitchell Stern
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Daniel Fried
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Dan Klein
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Direct Output Connection for a High-Rank Language Model
Sho Takase
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Jun Suzuki
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Masaaki Nagata
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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro
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Chris Dyer
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John Hale
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Dani Yogatama
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Stephen Clark
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Phil Blunsom
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Constituency Parsing with a Self-Attentive Encoder
Nikita Kitaev
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Dan Klein
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Finding syntax in human encephalography with beam search
John Hale
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Chris Dyer
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Adhiguna Kuncoro
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Jonathan Brennan
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Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing
Daniel Fried
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Dan Klein
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Linear-time Constituency Parsing with RNNs and Dynamic Programming
Juneki Hong
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Liang Huang
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An Empirical Study of Building a Strong Baseline for Constituency Parsing
Jun Suzuki
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Sho Takase
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Hidetaka Kamigaito
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Makoto Morishita
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Masaaki Nagata
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https://doi.org/10.18653/v1/P17-2025
https://doi.org/10.18653/v1/P17-2025
Field Of Study
Linguistic Trends
Embeddings
Approach
Generative Model
Discriminative Model
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