Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling (2020)

Abstract: Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax, and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntax-aware SRL relies on dependency representations of syntax. In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system. Nodes in our SpanGCN correspond to constituents. The computation is done in 3 stages. First, initial node representations are produced by ‘composing’ word representations of the first and last words in the constituent. Second, graph convolutions relying on the constituent tree are performed, yielding syntactically-informed constituent representations. Finally, the constituent representations are ‘decomposed’ back into word representations, which are used as input to the SRL classifier. We evaluate SpanGCN against alternatives, including a model using GCNs over dependency trees, and show its effectiveness on standard English SRL benchmarks CoNLL-2005, CoNLL-2012, and FrameNet.

## introduction the task of semantic role labeling (srl) involves predicting the predicate-argument structure of a sentence. more formally, for every predicate, the srl model must identify all argument spans and label them with their semantic roles (see figure 1 ). the most popular resources for estimating srl models are propbank @xcite and framenet @xcite . in both cases, annotations are made on top of syntactic constituent structures. earlier work on srl hinged ## constituency tree encoding the architecture for encoding constituency trees uses two building blocks, a bidirectional lstm for encoding sequences and a graph convolutional network for encoding graph structures. ## semantic role labeling model for both propbank and framenet, we use the same model architecture. ## conclusions in this paper, we introduced spangcn, a novel neural architecture for encoding constituency syntax at the word level. we applied spangcn to srl, on propbank and framenet. we observed substantial improvements from using constituent syntax on both datasets, and also in the realistic out-of-domain setting. by comparing to dependency gcn, we observed that for srl constituent structures yield more informative features that the dependency ones. given that gcns over dependency and constituency structure have access to very different information, it would be interesting to see in future work if combining two types of representations can lead to further improvements. while we experimented only with constituency syntax, spangcn may be able to encode any kind of span structure, for example, co-reference graphs, and can be used to produce linguistically-informed encoders for other nlp tasks rather than only srl.