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.

本研究从四个角度评估了收集上下文信息的四种聚合器对更大背景的训练的影响，并提出了一个属性辅助评估方法来解释更大背景训练带来的改进，旨在加深人们对更大背景的培训的理解和启发更多的后续工作。