Consistency of a model—that is, the invariance of its behavior under meaning-preserving
alternations in its input—is a highly desirable property in natural language processing. In …

We propose a new deterministic approach to coreference resolution that combines the
global information and precise features of modern machine-learning models with the …

Coreference resolution is a key problem in natural language understanding that still
escapes reliable solutions. One fundamental difficulty has been that of resolving instances …

Coreference Resolution is an essential task for Natural Language Processing (NLP)
application, which has a paramount impact on the performance of text summarization …

Coreference resolution is a well known clustering task in Natural Language Processing. In
this paper, we describe the Latent Left Linking model (L3M), a novel, principled, and …

In coreference resolution, a fair amount of research treats mention detection as a
preprocessed step and focuses on developing algorithms for clustering coreferred mentions …

Coreference resolution metrics quantify errors but do not analyze them. Here, we consider
an automated method of categorizing errors in the output of a coreference system into …

We describe a structure learning system for unrestricted coreference resolution that explores
two key modeling techniques: latent coreference trees and automatic entropy-guided feature …

Prompt-based methods have been used extensively across NLP to build zero-and few-shot
label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of …

The CoNLL-2012 shared task is an extension of the last year's coreference task. We
participated in the closed track of the shared tasks in both years. In this paper, we present …