  Semantic role labeling (SRL) focuses on recognizing the predicate-argument
structure of a sentence and plays a critical role in many natural language
processing tasks such as machine translation and question answering.
Practically all available methods do not perform full SRL, since they rely on
pre-identified predicates, and most of them follow a pipeline strategy, using
specific models for undertaking one or several SRL subtasks. In addition,
previous approaches have a strong dependence on syntactic information to
achieve state-of-the-art performance, despite being syntactic trees equally
hard to produce. These simplifications and requirements make the majority of
SRL systems impractical for real-world applications. In this article, we
propose the first transition-based SRL approach that is capable of completely
processing an input sentence in a single left-to-right pass, with neither
leveraging syntactic information nor resorting to additional modules. Thanks to
our implementation based on Pointer Networks, full SRL can be accurately and
efficiently done in $O(n^2)$, achieving the best performance to date on the
majority of languages from the CoNLL-2009 shared task.

Computation and Language