Syntax-Enhanced Self-Attention-Based Semantic Role Labeling
This work addresses the challenge of improving SRL accuracy for NLP applications, though it appears incremental as it builds on existing methods with syntactic enhancements.
The paper tackled the problem of effectively incorporating syntactic knowledge into semantic role labeling (SRL) for Chinese, achieving a new state-of-the-art performance on the CoNLL-2009 dataset.
As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of encoding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we conduct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.