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 …

The research focus of computational coreference resolution has exhibited a shift from
heuristic approaches to machine learning approaches in the past decade. This paper …

Statistical significance testing is a standard statistical tool designed to ensure that
experimental results are not coincidental. In this opinion/theoretical paper we discuss the …

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 …

In this paper, we present CorefQA, an accurate and extensible approach for the coreference
resolution task. We formulate the problem as a span prediction task, like in question …

The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and
Arabic, using the final version, v5. 0, of the OntoNotes corpus. It was a follow-on to the …

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

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …