Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling
This work improves SRL, a key NLP task, by integrating GCNs with LSTMs, though it is incremental as it builds on existing methods.
The paper tackled semantic role labeling by incorporating syntactic information using graph convolutional networks (GCNs) over dependency trees, achieving state-of-the-art scores on the CoNLL-2009 benchmark for both Chinese and English.
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.