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Front matter |
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Syntactic Parsing in Humans and Machines Paola Merlo |
pp. 1‑1 |
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Distilling Neural Networks for Greener and Faster Dependency Parsing Mark Anderson and Carlos Gómez-Rodríguez |
pp. 2‑13 |
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End-to-End Negation Resolution as Graph Parsing Robin Kurtz, Stephan Oepen and Marco Kuhlmann |
pp. 14‑24 |
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Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era Agnieszka Falenska, Anders Björkelund and Jonas Kuhn |
pp. 25‑39 |
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Semi-supervised Parsing with a Variational Autoencoding Parser Xiao Zhang and Dan Goldwasser |
pp. 40‑47 |
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Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction Lifeng Jin and William Schuler |
pp. 48‑61 |
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Obfuscation for Privacy-preserving Syntactic Parsing Zhifeng Hu, Serhii Havrylov, Ivan Titov and Shay B. Cohen |
pp. 62‑72 |
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Tensors over Semirings for Latent-Variable Weighted Logic Programs Esma Balkir, Daniel Gildea and Shay B. Cohen |
pp. 73‑90 |
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Advances in Using Grammars with Latent Annotations for Discontinuous Parsing Kilian Gebhardt |
pp. 91‑97 |
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Lexicalization of Probabilistic Linear Context-free Rewriting Systems Richard Mörbitz and Thomas Ruprecht |
pp. 98‑104 |
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Self-Training for Unsupervised Parsing with PRPN Anhad Mohananey, Katharina Kann and Samuel R. Bowman |
pp. 105‑110 |
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Span-Based LCFRS-2 Parsing Miloš Stanojević and Mark Steedman |
pp. 111‑121 |
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Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean Tae Hwan Oh, Ji Yoon Han, Hyonsu Choe, Seokwon Park, Han He, Jinho D. Choi, Na-Rae Han, Jena D. Hwang and Hansaem Kim |
pp. 122‑131 |
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Statistical Deep Parsing for Spanish Using Neural Networks Luis Chiruzzo and Dina Wonsever |
pp. 132‑144 |
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The Importance of Category Labels in Grammar Induction with Child-directed Utterances Lifeng Jin and William Schuler |
pp. 145‑150 |
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Overview of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies Gosse Bouma, Djamé Seddah and Daniel Zeman |
pp. 151‑161 |
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Turku Enhanced Parser Pipeline: From Raw Text to Enhanced Graphs in the IWPT 2020 Shared Task Jenna Kanerva, Filip Ginter and Sampo Pyysalo |
pp. 162‑173 |
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Hybrid Enhanced Universal Dependencies Parsing Johannes Heinecke |
pp. 174‑180 |
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Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing Han He and Jinho D. Choi |
pp. 181‑191 |
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Efficient EUD Parsing Mathieu Dehouck, Mark Anderson and Carlos Gómez-Rodríguez |
pp. 192‑205 |
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Linear Neural Parsing and Hybrid Enhancement for Enhanced Universal Dependencies Giuseppe Attardi, Daniele Sartiano and Maria Simi |
pp. 206‑214 |
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Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data Xinyu Wang, Yong Jiang and Kewei Tu |
pp. 215‑220 |
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How Much of Enhanced UD Is Contained in UD? Adam Ek and Jean-Philippe Bernardy |
pp. 221‑226 |
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The ADAPT Enhanced Dependency Parser at the IWPT 2020 Shared Task James Barry, Joachim Wagner and Jennifer Foster |
pp. 227‑235 |
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Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding Daniel Hershcovich, Miryam de Lhoneux, Artur Kulmizev, Elham Pejhan and Joakim Nivre |
pp. 236‑244 |
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RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English Stefan Grünewald and Annemarie Friedrich |
pp. 245‑252 |
Last modified on June 8, 2020, 6:47 a.m.