Deep Semantic Role Labeling: What Works and What's Next

Abstract

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on the CoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.

DOI: 10.18653/v1/P17-1044

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Cite this paper

@inproceedings{He2017DeepSR, title={Deep Semantic Role Labeling: What Works and What's Next}, author={Luheng He and Kenton Lee and Mike Lewis and Luke S. Zettlemoyer}, booktitle={ACL}, year={2017} }