论文标题
揭示核心分辨率高阶推断的神话
Revealing the Myth of Higher-Order Inference in Coreference Resolution
论文作者
论文摘要
本文分析了高阶推断（HOI）对核心分辨率任务的影响。 HOI几乎被几乎所有最近的核心分辨率模型都改编而成，而没有对其对代表学习的真正有效性进行大量研究。为了进行全面的分析，我们实施了端到端的核心系统以及四种HOI方法，参加了先行，实体均衡，跨度群集和聚类合并，后两个是我们的原始方法。我们发现，鉴于Spanbert等高性能编码器，HOI的影响对边缘是负面的，为这项任务提供了新的HOI观点。我们使用集群合并的最佳模型显示了Conll 2012上的80.2的AVG-F1，英语共享任务数据集。
This paper analyzes the impact of higher-order inference (HOI) on the task of coreference resolution. HOI has been adapted by almost all recent coreference resolution models without taking much investigation on its true effectiveness over representation learning. To make a comprehensive analysis, we implement an end-to-end coreference system as well as four HOI approaches, attended antecedent, entity equalization, span clustering, and cluster merging, where the latter two are our original methods. We find that given a high-performing encoder such as SpanBERT, the impact of HOI is negative to marginal, providing a new perspective of HOI to this task. Our best model using cluster merging shows the Avg-F1 of 80.2 on the CoNLL 2012 shared task dataset in English.
