Syntax-aware Semantic Role Labeling without Parsing

Syntax-aware Semantic Role Labeling without Parsing Rui Cai and Mirella Lapata Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB Rui.Cai@ed.ac.uk mlap@inf.ed.ac.uk Abstract In this paper we focus on learning dependency aware representations for semantic role label- ing without recourse to an external parser. The backbone of our model is an LSTM- based semantic role labeler jointly trained with two auxiliary tasks: predicting the dependency label of a word and whether there exists an arc linking it to the predicate. The auxiliary tasks provide syntactic information that is specific to semantic role labeling and are learned from training data (dependency annotations) with- out relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimen- tal results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish. 1 Introduction Semantic role labeling (SRL) aims to identify the arguments of semantic predicates in a sen- tence and label them with a set of predefined relations (e.g., ‘‘who’’ did ‘‘what’’ to ‘‘whom,’’ ‘‘when,’’ and ‘‘where’’). Semantic roles capture basic predicate-argument structure while abstract- ing over surface syntactic configurations and have been shown to benefit a wide spectrum of applications ranging from machine translation (Aziz et al., 2011; Marcheggiani et al., 2018) to information extraction (Christensen et al., 2011) and summarization (Khan et al., 2015). The successful application of neural networks to a variety of NLP tasks (Bahdanau et al., 2015; Vinyals et al., 2015) has provided strong impetus to develop deep end-to-end models for SRL that forego the need for extensive feature engineering. Recently proposed models (Zhou and Xu, 2015; He et al., 2017; Marcheggiani et al., 2017) largely rely on bi-directional recurrent neural networks (Hochreiter and Schmidhuber, 1997) and predict semantic roles from textual input. They achieve competitive results while being syntax agnostic, thereby challenging conventional wisdom that parse trees provide a better form of representation for assigning semantic role labels (Johansson and Nugues, 2008). There are, however, good reasons why syntax ought to help semantic role labeling. First and foremost, SRL systems are trained on datasets whose semantic role annotations have been pro- duced on top of treebanked corpora, and as a re- sult are closely tied to syntactic information. An example sentence with roles labeled in the style of PropBank (Palmer et al., 2005) is shown in Figure 1. Here, many arcs in the syntactic depen- dencygrapharemirroredinthesemanticdependency graph, suggesting that syntactic dependencies could provide useful information to the SRL task. Secondly, predicates are typically associated with a standard linking, that is, a deterministic mapping from syntactic roles to semantic ones (Lang and Lapata, 2010; Surdeanu et al., 2008). For example, subject (SBJ) is commonly mapped onto A0, whereas A1 is often realized as object (OBJ). Even in cases where there is no canoni- cal mapping, dependency labels are still closely related to certain semantic roles, like …

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