  We put forward an end-to-end multi-step machine learning model which jointly
labels semantic roles and the proto-roles of Dowty (1991), given a sentence and
the predicates therein. Our best architecture first learns argument spans
followed by learning the argument's syntactic heads. This information is shared
with the next steps for predicting the semantic roles and proto-roles. We also
experiment with transfer learning from argument and head prediction to role and
proto-role labeling. We compare using static and contextual embeddings for
words, arguments, and sentences. Unlike previous work, our model does not
require pre-training or fine-tuning on additional tasks, beyond using
off-the-shelf (static or contextual) embeddings and supervision. It also does
not require argument spans, their semantic roles, and/or their gold syntactic
heads as additional input, because it learns to predict all these during
training. Our multi-task learning model raises the state-of-the-art predictions
for most proto-roles.
