Larger-Context Tagging: When and Why Does It Work?
It addresses the lack of consensus on the effectiveness of larger-context approaches in tagging tasks, which limits their applicability, but is incremental as it focuses on understanding rather than achieving state-of-the-art performance.
The paper investigates when and why larger-context training improves tagging systems, conducting a comparative study on four aggregators and an attribute-aided evaluation method across four tagging tasks and thirteen datasets.
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.