  Large autoregressive generative models have emerged as the cornerstone for
achieving the highest performance across several Natural Language Processing
tasks. However, the urge to attain superior results has, at times, led to the
premature replacement of carefully designed task-specific approaches without
exhaustive experimentation. The Coreference Resolution task is no exception;
all recent state-of-the-art solutions adopt large generative autoregressive
models that outperform encoder-based discriminative systems. In this work,we
challenge this recent trend by introducing Maverick, a carefully designed - yet
simple - pipeline, which enables running a state-of-the-art Coreference
Resolution system within the constraints of an academic budget, outperforming
models with up to 13 billion parameters with as few as 500 million parameters.
Maverick achieves state-of-the-art performance on the CoNLL-2012 benchmark,
training with up to 0.006x the memory resources and obtaining a 170x faster
inference compared to previous state-of-the-art systems. We extensively
validate the robustness of the Maverick framework with an array of diverse
experiments, reporting improvements over prior systems in data-scarce,
long-document, and out-of-domain settings. We release our code and models for
research purposes at https://github.com/SapienzaNLP/maverick-coref.
