Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends
Giuliano Martinelli, Edoardo Barba, Roberto Navigli

TL;DR
Maverick is a simple, resource-efficient coreference resolution system that outperforms large autoregressive models, achieving state-of-the-art results with significantly less memory and faster inference.
Contribution
The paper introduces Maverick, a lightweight pipeline that challenges recent trends by providing a more efficient yet highly effective coreference resolution method.
Findings
Outperforms models with up to 13 billion parameters
Uses only 0.006x the memory resources of previous systems
Achieves 170x faster inference than prior state-of-the-art models
Abstract
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…
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Taxonomy
TopicsNeural Networks and Applications · Speech Recognition and Synthesis · Machine Learning and Data Classification
