Centering theory (CT; Grosz et al., 1995) provides a linguistic analysis of
the structure of discourse. According to the theory, local coherence of
discourse arises from the manner and extent to which successive utterances make
reference to the same entities. In this paper, we investigate the connection
between centering theory and modern coreference resolution systems. We provide
an operationalization of centering and systematically investigate if neural
coreference resolvers adhere to the rules of centering theory by defining
various discourse metrics and developing a search-based methodology. Our
information-theoretic analysis reveals a positive dependence between
coreference and centering; but also shows that high-quality neural coreference
resolvers may not benefit much from explicitly modeling centering ideas. Our
analysis further shows that contextualized embeddings contain much of the
coherence information, which helps explain why CT can only provide little gains
to modern neural coreference resolvers which make use of pretrained
representations. Finally, we discuss factors that contribute to coreference
which are not modeled by CT such as world knowledge and recency bias. We
formulate a version of CT that also models recency and show that it captures
coreference information better compared to vanilla CT.

本文探讨了中心原理（CT）与现代指称消解系统之间的关系，并提供了中心的操作性定义。信息熵分析表明，指称消解与中心原理之间存在正相关依赖关系，但高质量的神经指称消解器对中心概念的显式建模没有太多好处。结果表明，语境化嵌入包含了很多一致性信息，这有助于解释为什么 CT 只能对使用预训练表示的现代神经指称消解器提供少量收益。同时，本文还讨论了 CT 未能对世界知识和近期偏见等对指称消解的贡献建模，并提出了一种同时考虑最近发生的偏见的中心模型，这种模型在捕捉指称消解信息时比基本的 CT 模型更有效。