A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling

TL;DR

This paper introduces a syntax-aware multi-task learning framework for Chinese semantic role labeling that leverages dependency parsing to enhance SRL performance, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper proposes a novel multi-task learning framework that extracts implicit syntactic representations from a dependency parser to improve Chinese SRL performance.

Findings

Achieves new state-of-the-art F1 scores of 87.54 and 88.5 on benchmark datasets.

Effectively improves SRL performance by integrating syntactic information.

Demonstrates the effectiveness of implicit syntactic representations in SRL.

Abstract

Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline. Besides, we present a MTL framework that includes the basic SRL module and a dependency parser module. Different from the commonly used hard parameter sharing strategy in MTL, the main idea is to extract implicit syntactic representations from the dependency parser as external inputs for the basic SRL model. Experiments on the benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show that our proposed framework can effectively improve the performance over the strong baselines. With the external BERT representations, our framework achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of the two benchmarks, respectively. In-depth analysis are conducted to gain more insights on the proposed framework and the effectiveness of syntax.