Deep Contextualized Word Embeddings in Transition-Based and Graph-Based Dependency Parsing - A Tale of Two Parsers Revisited
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Joakim Nivre | Miryam de Lhoneux | Artur Kulmizev | Johannes Gontrum | Elena Fano | Joakim Nivre | Artur Kulmizev | Johannes Gontrum | Elena Fano
[1] Joakim Nivre,et al. Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle , 2017, IWPT.
[2] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[3] Joakim Nivre,et al. An Efficient Algorithm for Projective Dependency Parsing , 2003, IWPT.
[4] Fernando Pereira,et al. Non-Projective Dependency Parsing using Spanning Tree Algorithms , 2005, HLT.
[5] Joakim Nivre,et al. An Investigation of the Interactions Between Pre-Trained Word Embeddings, Character Models and POS Tags in Dependency Parsing , 2018, EMNLP.
[6] Joakim Nivre,et al. Characterizing the Errors of Data-Driven Dependency Parsing Models , 2007, EMNLP.
[7] John D. Lafferty,et al. Towards History-based Grammars: Using Richer Models for Probabilistic Parsing , 1993, ACL.
[8] Timothy Dozat,et al. Universal Dependency Parsing from Scratch , 2019, CoNLL.
[9] Jonas Kuhn,et al. The Best of Both Worlds – A Graph-based Completion Model for Transition-based Parsers , 2012, EACL.
[10] Joakim Nivre,et al. A Dynamic Oracle for Arc-Eager Dependency Parsing , 2012, COLING.
[11] Joakim Nivre,et al. Algorithms for Deterministic Incremental Dependency Parsing , 2008, CL.
[12] Felice Dell'Orletta,et al. Accurate Dependency Parsing with a Stacked Multilayer Perceptron , 2009 .
[13] Yonatan Belinkov,et al. Linguistic Knowledge and Transferability of Contextual Representations , 2019, NAACL.
[14] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[15] Luke S. Zettlemoyer,et al. Deep Contextualized Word Representations , 2018, NAACL.
[16] Joakim Nivre,et al. 82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models , 2018, CoNLL.
[17] Sabine Buchholz,et al. CoNLL-X Shared Task on Multilingual Dependency Parsing , 2006, CoNLL.
[18] Noah A. Smith,et al. Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs , 2015, EMNLP.
[19] Tomas Mikolov,et al. Enriching Word Vectors with Subword Information , 2016, TACL.
[20] Walter Daelemans,et al. A Memory-Based Alternative for Connectionist Shift-Reduce Parsing , 2000 .
[21] Hao Zhang,et al. Generalized Higher-Order Dependency Parsing with Cube Pruning , 2012, EMNLP.
[22] Slav Petrov,et al. Globally Normalized Transition-Based Neural Networks , 2016, ACL.
[23] Giorgio Satta,et al. Dynamic Programming Algorithms for Transition-Based Dependency Parsers , 2011, ACL.
[24] Kenji Sagae,et al. Dynamic Programming for Linear-Time Incremental Parsing , 2010, ACL.
[25] Jason Eisner,et al. Three New Probabilistic Models for Dependency Parsing: An Exploration , 1996, COLING.
[26] Joakim Nivre,et al. Pseudo-Projective Dependency Parsing , 2005, ACL.
[27] Slav Petrov,et al. Structured Training for Neural Network Transition-Based Parsing , 2015, ACL.
[28] Christopher D. Manning,et al. A Structural Probe for Finding Syntax in Word Representations , 2019, NAACL.
[29] Alon Lavie,et al. Parser Combination by Reparsing , 2006, NAACL.
[30] Ivan Titov,et al. A Latent Variable Model for Generative Dependency Parsing , 2007, Trends in Parsing Technology.
[31] Xavier Carreras,et al. Experiments with a Higher-Order Projective Dependency Parser , 2007, EMNLP.
[32] Omer Levy,et al. GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.
[33] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[34] Timothy Dozat,et al. Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task , 2017, CoNLL.
[35] Regina Barzilay,et al. Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing , 2019, NAACL.
[36] Yoav Goldberg,et al. Assessing BERT's Syntactic Abilities , 2019, ArXiv.
[37] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[38] Joakim Nivre,et al. Analyzing the Effect of Global Learning and Beam-Search on Transition-Based Dependency Parsing , 2012, COLING.
[39] Richard Johansson,et al. Incremental Dependency Parsing Using Online Learning , 2007, EMNLP-CoNLL.
[40] Yuji Matsumoto,et al. Statistical Dependency Analysis with Support Vector Machines , 2003, IWPT.
[41] Eliyahu Kiperwasser,et al. Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations , 2016, TACL.
[42] Alexander M. Rush,et al. Dual Decomposition for Parsing with Non-Projective Head Automata , 2010, EMNLP.
[43] Joakim Nivre,et al. Non-Projective Dependency Parsing in Expected Linear Time , 2009, ACL.
[44] Joakim Nivre,et al. Integrating Graph-Based and Transition-Based Dependency Parsers , 2008, ACL.
[45] Stephen Clark,et al. A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing , 2008, EMNLP.
[46] Noah A. Smith,et al. Transition-Based Dependency Parsing with Stack Long Short-Term Memory , 2015, ACL.
[47] Fernando Pereira,et al. Online Learning of Approximate Dependency Parsing Algorithms , 2006, EACL.
[48] Martin Potthast,et al. CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies , 2018, CoNLL.
[49] Benjamin Müller,et al. ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing , 2018, CoNLL Shared Task.
[50] Jonas Kuhn,et al. The (Non-)Utility of Structural Features in BiLSTM-based Dependency Parsers , 2019, ACL.
[51] Yijia Liu,et al. Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation , 2018, CoNLL.
[52] Alex Wang,et al. What do you learn from context? Probing for sentence structure in contextualized word representations , 2019, ICLR.
[53] Joakim Nivre,et al. Labeled Pseudo-Projective Dependency Parsing with Support Vector Machines , 2006, CoNLL.
[54] Joakim Nivre,et al. Training Deterministic Parsers with Non-Deterministic Oracles , 2013, TACL.
[55] David Gil,et al. The World Atlas of Language Structures , 2005 .
[56] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[57] Ming-Wei Chang,et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.
[58] Danqi Chen,et al. A Fast and Accurate Dependency Parser using Neural Networks , 2014, EMNLP.
[59] Sebastian Riedel,et al. The CoNLL 2007 Shared Task on Dependency Parsing , 2007, EMNLP.
[60] Changki Lee,et al. SEx BiST: A Multi-Source Trainable Parser with Deep Contextualized Lexical Representations , 2018, CoNLL Shared Task.
[61] Prakhar Gupta,et al. Learning Word Vectors for 157 Languages , 2018, LREC.
[62] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[63] Fernando Pereira,et al. Multilingual Dependency Analysis with a Two-Stage Discriminative Parser , 2006, CoNLL.
[64] Christof Monz,et al. The Importance of Being Recurrent for Modeling Hierarchical Structure , 2018, EMNLP.
[65] Joakim Nivre,et al. From Raw Text to Universal Dependencies - Look, No Tags! , 2017, CoNLL.
[66] Joakim Nivre,et al. Old School vs. New School: Comparing Transition-Based Parsers with and without Neural Network Enhancement , 2017, TLT.
[67] Timothy Dozat,et al. Deep Biaffine Attention for Neural Dependency Parsing , 2016, ICLR.
[68] Sarah Armstrong,et al. How do you Learn , 2016 .
[69] Joakim Nivre,et al. Analyzing and Integrating Dependency Parsers , 2011, CL.
[70] Koby Crammer,et al. Online Large-Margin Training of Dependency Parsers , 2005, ACL.
[71] Daniel Kondratyuk,et al. 75 Languages, 1 Model: Parsing Universal Dependencies Universally , 2019, EMNLP.