Deep learning methods employ multiple processing layers to learn hierarchical
representations of data, and have produced state-of-the-art results in many domains …

Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no
explicit linguistic features. However, prior work has shown that gold syntax trees can …

We introduce a new deep learning model for semantic role labeling (SRL) that significantly
improves the state of the art, along with detailed analyses to reveal its strengths and …

Abstract Semantic Role Labeling (SRL) is believed to be a crucial step towards natural
language understanding and has been widely studied. Recent years, end-to-end SRL with …

Semantic role labeling (SRL) is one of the basic natural language processing (NLP)
problems. To this date, most of the successful SRL systems were built on top of some form of …

Recent BIO-tagging-based neural semantic role labeling models are very high performing,
but assume gold predicates as part of the input and cannot incorporate span-level features …

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 …

We introduce the syntactic scaffold, an approach to incorporating syntactic information into
semantic tasks. Syntactic scaffolds avoid expensive syntactic processing at runtime, only …

We present a simple and accurate span-based model for semantic role labeling (SRL). Our
model directly takes into account all possible argument spans and scores them for each …

Abstract Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family
of structured prediction models (Belanger and McCallum, 2016). An energy function over …