Syntax-aware Semantic Role Labeling without Parsing

Rui Cai,Mirella Lapata

doi:10.1162/tacl_a_00272

Abstract

In this paper we focus on learning dependency aware representations for semantic role labeling 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) without relying on existing dependency parsers, which can be noisy (e.g., on out-of-domain data or infrequent constructions). Experimental 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.

Highlights

Semantic role labeling (SRL) aims to identify the arguments of semantic predicates in a sentence and label them with a set of predefined relations (e.g., ‘‘who’’ did ‘‘what’’ to ‘‘whom,’’ ‘‘when,’’ and ‘‘where’’)
Experimental results on the CoNLL-2009 benchmark dataset show that our model is able to outperform the state of the art in English, and to improve SRL performance in other languages, including Chinese, German, and Spanish
We report the results of two strong symbolic models based on tensor factorization (Lei et al, 2015) and a pipeline of modules that carry out the tokenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling (Bjorkelund et al, 2010)

Summary

Introduction

Semantic role labeling (SRL) aims to identify the arguments of semantic predicates in a sentence and label them with a set of predefined relations (e.g., ‘‘who’’ did ‘‘what’’ to ‘‘whom,’’ ‘‘when,’’ and ‘‘where’’). 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). Even in cases where there is no canonical mapping, dependency labels are still closely related to certain semantic roles, like the syntactic function TMP and the semantic role AM-TMP

Objectives

Methods

Results

Conclusion