Linguistically-Informed Self-Attention for Semantic Role Labeling
Table 1: Precision, recall and F1 on the CoNLL-2005 development and test sets.
['GloVe', 'Dev P', 'Dev R', 'Dev F1', 'WSJ Test P', 'WSJ Test R', 'WSJ Test F1', 'Brown Test P', 'Brown Test R', 'Brown Test F1']
For models using GloVe embeddings, our syntax-free SA model already achieves a new state-of-the-art by jointly predicting predicates, POS and SRL. LISA with its own parses performs comparably to SA, but when supplied with D&M parses LISA out -performs the previous state-of-the-art by 2.5 F1 points. On the out-of-domain Brown test set, LISA also performs comparably to its syntax-free counterpart with its own parses, but with D&M parses LISA performs exceptionally well, more than 3.5 F1 points higher than He et al. Incorporating ELMo embeddings improves all scores. The gap in SRL F1 between models using LISA and D&M parses is smaller due to LISA’s In both settings LISA leverages domain-agnostic syntactic information rather than over-fitting to the newswire training data which leads to high performance even on out-of-domain text.