Coreference resolution aims at resolving repeated references to an object in a document
and forms a core component of natural language processing (NLP) research. When used as …

Large pre-trained neural networks such as BERT have had great recent success in NLP,
motivating a growing body of research investigating what aspects of language they are able …

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes
(+ 3.9 F1) and GAP (+ 11.5 F1) benchmarks. A qualitative analysis of model predictions …

We introduce the first end-to-end coreference resolution model and show that it significantly
outperforms all previous work without using a syntactic parser or hand-engineered mention …

We present an empirical study of gender bias in coreference resolution systems. We first
introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by …

We introduce a fully differentiable approximation to higher-order inference for coreference
resolution. Our approach uses the antecedent distribution from a span-ranking architecture …

Interpreting anaphoric references is a fundamental aspect of our language competence that
has long attracted the attention of computational linguists. The appearance of ever-larger …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

Coreference resolution systems are typically trained with heuristic loss functions that require
careful tuning. In this paper we instead apply reinforcement learning to directly optimize a …

A long-standing challenge in coreference resolution has been the incorporation of entity-
level information-features defined over clusters of mentions instead of mention pairs. We …