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.

Larger-Context Tagging: When and Why Does It Work?



<i>**Session Chair: ** Sheng Zhang </i><br><br>

4E-Oral: Syntax: Tagging, Chunking, and Parsing

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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