  There are many different ways in which external information might be used in
an NLP task. This paper investigates how external syntactic information can be
used most effectively in the Semantic Role Labeling (SRL) task. We evaluate
three different ways of encoding syntactic parses and three different ways of
injecting them into a state-of-the-art neural ELMo-based SRL sequence labelling
model. We show that using a constituency representation as input features
improves performance the most, achieving a new state-of-the-art for
non-ensemble SRL models on the in-domain CoNLL'05 and CoNLL'12 benchmarks.
