Simpler but More Accurate Semantic Dependency Parsing

While syntactic dependency annotations concentrate on the surface or functional structure of a sentence, semantic dependency annotations aim to capture between-word relationships that are more closely related to the meaning of a sentence, using graph-structured representations. We extend the LSTM-based syntactic parser of Dozat and Manning (2017) to train on and generate these graph structures. The resulting system on its own achieves state-of-the-art performance, beating the previous, substantially more complex state-of-the-art system by 0.6% labeled F1. Adding linguistically richer input representations pushes the margin even higher, allowing us to beat it by 1.9% labeled F1.

[1]  Alfred V. Aho,et al.  The Theory of Parsing, Translation, and Compiling , 1972 .

[2]  Daniel Gildea,et al.  Automatic Labeling of Semantic Roles , 2000, ACL.

[3]  Joakim Nivre,et al.  An Efficient Algorithm for Projective Dependency Parsing , 2003, IWPT.

[4]  Jun'ichi Tsujii,et al.  Deep Linguistic Analysis for the Accurate Identification of Predicate-Argument Relations , 2004, COLING.

[5]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

[6]  Stephan Oepen,et al.  Discriminant-Based MRS Banking , 2006, LREC.

[7]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[8]  Noah A. Smith,et al.  Dual Decomposition with Many Overlapping Components , 2011, EMNLP.

[9]  Marie Mikulová,et al.  Announcing Prague Czech-English Dependency Treebank 2.0 , 2012, LREC.

[10]  Andrew McCallum,et al.  Transition-based Dependency Parsing with Selectional Branching , 2013, ACL.

[11]  Stephan Oepen,et al.  SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing , 2014, *SEMEVAL.

[12]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[13]  André F. T. Martins,et al.  Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data , 2015, *SEMEVAL.

[14]  Weiwei Sun,et al.  Peking: Building Semantic Dependency Graphs with a Hybrid Parser , 2015, SemEval@NAACL-HLT.

[15]  Noah A. Smith,et al.  Transition-Based Dependency Parsing with Stack Long Short-Term Memory , 2015, ACL.

[16]  Yusuke Miyao,et al.  SemEval 2015 Task 18: Broad-Coverage Semantic Dependency Parsing , 2015, *SEMEVAL.

[17]  Young-Bum Kim,et al.  Frustratingly Easy Neural Domain Adaptation , 2016, COLING.

[18]  Noah A. Smith,et al.  Training with Exploration Improves a Greedy Stack LSTM Parser , 2016, EMNLP.

[19]  Christopher D. Manning,et al.  Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks , 2016, LREC.

[20]  Sampo Pyysalo,et al.  Universal Dependencies v1: A Multilingual Treebank Collection , 2016, LREC.

[21]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[22]  Benoît Sagot,et al.  Paris and Stanford at EPE 2017: Downstream Evaluation of Graph-based Dependency Representations , 2017 .

[23]  Noah A. Smith,et al.  Deep Multitask Learning for Semantic Dependency Parsing , 2017, ACL.

[24]  Timothy Dozat,et al.  Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task , 2017, CoNLL.

[25]  Timothy Dozat,et al.  Deep Biaffine Attention for Neural Dependency Parsing , 2016, ICLR.

[26]  Bruno Guillaume,et al.  Enhanced UD Dependencies with Neutralized Diathesis Alternation , 2017, DepLing.

[27]  Mark Steedman,et al.  Universal Semantic Parsing , 2017, EMNLP.

[28]  Wanxiang Che,et al.  A Neural Transition-Based Approach for Semantic Dependency Graph Parsing , 2018, AAAI.

[29]  Jingzhou Liu,et al.  Stack-Pointer Networks for Dependency Parsing , 2018, ACL.