Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold.

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 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 pipelining, and achieves state-of-the-art performance.