Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

Diego Marcheggiani, Ivan Titov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the semantic representations are closely related to syntactic ones, we exploit syntactic information in our model. We propose a version of graph convolutional networks (GCNs), a recent class of neural networks operating on graphs, suited to model syntactic dependency graphs. GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence. We observe that GCN layers are complementary to LSTM ones: when we stack both GCN and LSTM layers, we obtain a substantial improvement over an already state-of-the art LSTM SRL model, resulting in the best reported scores on the standard benchmark (CoNLL-2009) both for Chinese and English.
Original languageEnglish
Title of host publicationProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 2017)
Place of PublicationCopenhagen, Denmark
PublisherAssociation for Computational Linguistics
Pages1506–1515
Number of pages10
ISBN (Electronic)978-1-945626-83-8
DOIs
Publication statusPublished - 11 Sep 2017
EventEMNLP 2017: Conference on Empirical Methods in Natural Language Processing - Copenhagen, Denmark
Duration: 7 Sep 201711 Sep 2017
http://emnlp2017.net/index.html
http://emnlp2017.net/

Conference

ConferenceEMNLP 2017: Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2017
CountryDenmark
CityCopenhagen
Period7/09/1711/09/17
Internet address

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