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This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT.
Semi-Supervised Sequence Modeling with Cross-View Training
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SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing
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Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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What Do Recurrent Neural Network Grammars Learn About Syntax?
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Neural Joint Model for Transition-based Chinese Syntactic Analysis
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Contextual String Embeddings for Sequence Labeling
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Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping
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Dynamic Feature Induction: The Last Gist to the State-of-the-Art
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Simpler but More Accurate Semantic Dependency Parsing
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Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context
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Stack-Pointer Networks for Dependency Parsing
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Deep Contextualized Word Representations
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Improving Language Understanding by Generative Pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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A Neural Transition-Based Approach for Semantic Dependency Graph Parsing
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Deep Multitask Learning for Semantic Dependency Parsing
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