Neural Semantic Role Labeling with Dependency Path Embeddings

By Michael Roth, Mirella Lapata. 2016

194 citations recorded by the source corpus.

This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, such as nested subordinations and nominal predicates, are not handled well by existing models. Our model treats such instances as subsequences of lexicalized dependency paths and learns suitable embedding representations. We experimentally demonstrate that such embeddings can improve results over previous state-of-the-art semantic role labelers, and showcase qualitative improvements obtained by our method.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling (2017, methodology): “…in neural SRL models include: FitzGerald et al. (2015) who use standard syntactic features within an MLP calculating potentials of a CRF model; Roth and Lapata (2016) who enriched standard features for SRL with LSTM representations of syntactic paths between arguments and predicates; Lei et…”
Deep Semantic Role Labeling: What Works and What’s Next (2017, methodology): “More recently, neural methods have been employed on top of syntactic features (FitzGerald et al., 2015; Roth and Lapata, 2016).”
Question Answering as Global Reasoning Over Semantic Abstractions (2018, methodology): “• PredArg; for this view we used Verb-SRL and NomSRL(Punyakanok, Roth, and Yih 2008; Roth and Lapata 2016), Prep-SRL (Srikumar and Roth 2013), Comma-SRL (Arivazhagan, Christodoulopoulos, and Roth 2016).”
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking (2020, result): “Our ﬁndings are consistent with the literature, where dependency paths connecting predicate and argument represent strong features for SRL (Johansson & Nugues, 2008; Roth & Lapata, 2016).”
Improving Robustness by Augmenting Training Sentences with Predicate-Argument Structures (2020, background): “They use syntactic information for the tasks of SRL and coreference resolution.”
A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling (2017, background): “7 % F1 for PathLSTM (Roth and Lapata, 2016)) and approaches the best results overall ( 87 .”
A Joint Sequential and Relational Model for Frame-Semantic Parsing (2017, background): “Roth and Lapata (2016) further includes dependency path embeddings as features.”
Simple BERT Models for Relation Extraction and Semantic Role Labeling (2019, background): “State-of-the-art neural models for both tasks typically rely on lexical and syntactic features, such as part-of-speech tags (Marcheggiani et al., 2017), syntactic trees (Roth and Lapata, 2016; Zhang et al., 2018; Li et al., 2018), and global decoding constraints (Li et al., 2019).”
Cross-Sentence N-ary Relation Extraction with Graph LSTMs (2017, methodology): “Recently, neural models have been applied to semantic role labeling (FitzGerald et al., 2015; Roth and Lapata, 2016).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “Examples include NNF [174] and dependency path embedding [175].”