  We present a new, efficient frame-semantic parser that labels semantic
arguments to FrameNet predicates. Built using an extension to the segmental RNN
that emphasizes recall, our basic system achieves competitive performance
without any calls to a syntactic parser. We then introduce a method that uses
phrase-syntactic annotations from the Penn Treebank during training only,
through a multitask objective; no parsing is required at training or test time.
This "syntactic scaffold" offers a cheaper alternative to traditional syntactic
pipelining, and achieves state-of-the-art performance.
