大背景标记：何时以及为什么有效？
Larger-Context Tagging: When and Why Does It Work?
摘要：
神经网络和预训练技术的发展催生了许多在典型基准测试中实现卓越性能的高级标记系统。然而，一个相对较少讨论的话题是，如果更多的上下文信息被引入到当前得分最高的标记系统。虽然一些现有的作品已经试图将标记系统从文档级转移到文档级，但是关于何时以及为什么它起作用仍然没有一致的结论，这限制了大上下文方法在标记任务中的适用性。在本文中，我们不是通过架构探索来追求最先进的标记系统，而是专注于调查何时以及为什么大背景训练作为一种通用策略可以工作。为此，我们对四个用于收集上下文信息的聚合器进行了深入的比较研究，并提出了一种属性辅助评估方法来解释更大的上下文训练所带来的改进。在实验中，我们建立了一个基于四个标记任务和十三个数据集的测试床。希望我们的初步观察能加深对大语境训练的理解，并启发更多关于语境信息使用的后续工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
