We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and only using local inference. However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset. We also consider Chinese, Czech and Spanish where our approach also achieves competitive results. Syntactic parsers are unreliable on out-of-domain data, so standard (i.e., syntactically-informed) SRL models are hindered when tested in this setting. Our syntax-agnostic model appears more robust, resulting in the best reported results on standard out-of-domain test sets.
In the last years there has been a flurry of work that employed neural network approaches for SRL. used hand-crafted features within an MLP for calculating potentials of a CRF model; extended the features of a non-neural SRL model with LSTM representations of syntactic paths between arguments and predicates; relied on low-rank tensor factorization that captured interactions between arguments, predicate, their syntactic path and semantic roles; while and used convolutional networks as sentence encoder and a CRF as a role classifier, both approaches employed a rich set of features as input of the convolutional encoder. Finally, jointly modeled syntactic and semantic structures; they extended one of the earliest neural approaches for SRL @cite_2 @cite_10 @cite_8 , with more sophisticated modeling techniques, for example, using LSTMs instead of vanilla RNNs.
@cite_8 @cite_10 @cite_2
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Motivated by the large number of languages (seven) and the short development time (two months) of the 2009 CoNLL shared task, we exploited latent variables to avoid the costly process of hand-crafted feature engineering, allowing the latent variables to induce features from the data. We took a pre-existing generative latent variable model of joint syntactic-semantic dependency parsing, developed for English, and applied it to six new languages with minimal adjustments. The parser's robustness across languages indicates that this parser has a very general feature set. The parser's high performance indicates that its latent variables succeeded in inducing effective features. This system was ranked third overall with a macro averaged F1 score of 82.14 , only 0.5 worse than the best system. This demonstration presents a high-performance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the to-kenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system's two main components draw on improved versions of a state-of-the-art dependency parser (Bohnet, 2009) and semantic role labeler (, 2009) developed independently by the authors. The system takes a sentence as input and produces a syntactic and semantic annotation using the CoNLL 2009 format. The processing time needed for a sentence typically ranges from 10 to 1000 milliseconds. The predicate--argument structures in the final output are visualized in the form of segments, which are more intuitive for a user. We propose a solution to the challenge of the CoNLL 2008 shared task that uses a generative history-based latent variable model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic dependencies. The submitted model yields 79.1 macro-average F1 performance, for the joint task, 86.9 syntactic dependencies LAS and 71.0 semantic dependencies F1. A larger model trained after the deadline achieves 80.5 macro-average F1, 87.6 syntactic dependencies LAS, and 73.1 semantic dependencies F1.