  Semantic role labeling (SRL) involves extracting propositions (i.e.
predicates and their typed arguments) from natural language sentences.
State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not
model non-local interaction between arguments. We propose a new approach to
modeling these interactions while maintaining efficient inference.
Specifically, we use Capsule Networks: each proposition is encoded as a tuple
of \textit{capsules}, one capsule per argument type (i.e. role). These tuples
serve as embeddings of entire propositions. In every network layer, the
capsules interact with each other and with representations of words in the
sentence. Each iteration results in updated proposition embeddings and updated
predictions about the SRL structure. Our model substantially outperforms the
non-refinement baseline model on all 7 CoNLL-2019 languages and achieves
state-of-the-art results on 5 languages (including English) for dependency SRL.
We analyze the types of mistakes corrected by the refinement procedure. For
example, each role is typically (but not always) filled with at most one
argument. Whereas enforcing this approximate constraint is not useful with the
modern SRL system, iterative procedure corrects the mistakes by capturing this
intuition in a flexible and context-sensitive way.
