Attentive Semantic Role Labeling with Boundary Indicator
This work addresses semantic role labeling for natural language processing, but it is incremental as it builds on existing models with simple enhancements.
The paper tackled semantic role labeling by introducing auxiliary tags to enhance a syntax-agnostic model with multi-hop self-attention, achieving competitive performance with state-of-the-art models on CoNLL-2009 benchmarks for English and Chinese.
The goal of semantic role labeling (SRL) is to discover the predicate-argument structure of a sentence, which plays a critical role in deep processing of natural language. This paper introduces simple yet effective auxiliary tags for dependency-based SRL to enhance a syntax-agnostic model with multi-hop self-attention. Our syntax-agnostic model achieves competitive performance with state-of-the-art models on the CoNLL-2009 benchmarks both for English and Chinese.