Chinese Semantic Role Labeling Based on Dynamic Syntax Pruning

Semantic role labeling(SRL), as the shallow semantic parsing task, which has received extensive research attention in recent years and plays a core role in the natural language processing(NLP) community. The SRL task aims to identify the corresponding argument roles for the predicates of a given sentence, which can facilitate the downstream NLP tasks, such as information extraction, question answer system and reading comprehension, etc. A great number of methods have been proposed for the task, and the existing studies can be divided into two main categories: machine learning based methods with hand-crafted discrete features and deep learning methods with automatic distributed features. The early studies largely separate SRL into two individual subtasks, i.e., predicate disambiguation and argument role labeling. More recently, great efforts have been paid for constructing various end-to-end SRL architectures, solving two pipeline steps in one shot via one unified model. Recent studies also show that integrating external syntactic features, such as syntactic dependency trees, are important for the SRL task highly. So designing a novel neural model, which can capture syntactic features effectively, has become a heated research topic. Recently, He et al.(2018) find that only a part of syntactic structure information can offer valuable information for the SRL task, which calls for pruning the syntactic structure features. However, the existing work adopts the offline syntactic pruning strategy, which can inevitably lead to either the loss of key syntactic information or the weakening of pruning effectiveness. Extracting syntactic features, as an important step of the SRL task, will largely affect the final performance of the task. However, the existing neural network methods fail to effectively model syntactic features. For example, the existing studies adopt the offline syntactic pruning strategy with fixed human labor, which inevitably leads to the loss of key syntactic information or the weakening of pruning effectiveness. To address the above issues, we propose an end-to-end neural network model for the Chinese SRL task based on dynamic syntactic pruning mechanism. Specifically, we propose two novel methods: recursive neural network model with dynamic syntactic pruning(Recur-DSP) and syntax-label graph convolutional network with dynamic syntactic pruning(SGCN-DSP). Recur-DSP uses a recursive neural network model to encode and fuse syntactic structure knowledge, and applies the Gumbel-Softmax function to realize dynamic syntactic pruning. SGCN-DSP exploits a graph convolutional neural network model that can simultaneously encode syntactic arcs and labels, based on which we introduce the corresponding dynamic syntactic pruning strategy. Experimental results on multiple benchmark datasets show the effectiveness of the proposed methods. Our proposed methods outperform the current best method by a large margin, giving the state-of-the-art performances for the Chinese SRL task. Specifically, our proposed model SGCN-DSP achieves 86.9% F1 score in argument role labeling and 89.1% F1 score in predicate identification based on the CoNLL09 dataset. By integrating the current pre-trained language model BERT(Bidirectional Encoder Representation from Transformers, BERT), the task performance can be further improved. The proposed SGCN-DSP gives 90.4% F1 score in argument role labeling, and 90.8% F1 scores in predicate identification, respectively. © 2022, Science Press. All right reserved.