  The development of neural networks and pretraining techniques has spawned
many sentence-level tagging systems that achieved superior performance on
typical benchmarks. However, a relatively less discussed topic is what if more
context information is introduced into current top-scoring tagging systems.
Although several existing works have attempted to shift tagging systems from
sentence-level to document-level, there is still no consensus conclusion about
when and why it works, which limits the applicability of the larger-context
approach in tagging tasks. In this paper, instead of pursuing a
state-of-the-art tagging system by architectural exploration, we focus on
investigating when and why the larger-context training, as a general strategy,
can work.
  To this end, we conduct a thorough comparative study on four proposed
aggregators for context information collecting and present an attribute-aided
evaluation method to interpret the improvement brought by larger-context
training. Experimentally, we set up a testbed based on four tagging tasks and
thirteen datasets. Hopefully, our preliminary observations can deepen the
understanding of larger-context training and enlighten more follow-up works on
the use of contextual information.
