# Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends arXiv: https://arxiv.org/abs/2407.21489 ยท HTML: https://mlsanity.com/paper/2407.21489/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 58 references that are in the map. - First posted on arXiv 31 Jul 2024. ## Builds on (most cited first, 30 of 32) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018) - [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020) - [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021) - [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://mlsanity.com/paper/1804.04235/) (2018) - [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019) - [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017) - [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021) - [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018) - [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019) - [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://mlsanity.com/paper/1908.05803/) (2019) - [Context-Aware Neural Machine Translation Learns Anaphora Resolution](https://mlsanity.com/paper/1805.10163/) (2018) - [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019) - [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019) - [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021) - [Autoregressive Structured Prediction with Language Models](https://mlsanity.com/paper/2210.14698/) (2022) - [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021) - [Coreference-Aware Dialogue Summarization](https://mlsanity.com/paper/2106.08556/) (2021) - [Coreference Resolution through a seq2seq Transition-Based System](https://mlsanity.com/paper/2211.12142/) (2022) - [PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution](https://mlsanity.com/paper/1810.09807/) (2018) - [Neural Models for Reasoning over Multiple Mentions using Coreference](https://mlsanity.com/paper/1804.05922/) (2018) - [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022) - [On Generalization in Coreference Resolution](https://mlsanity.com/paper/2109.09667/) (2021) - [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022) - [Entity Linking and Discovery via Arborescence-based Supervised Clustering](https://mlsanity.com/paper/2109.01242/) (2021) - [Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering](https://mlsanity.com/paper/1805.04893/) (2018) - [Robust Question Answering Through Sub-part Alignment](https://mlsanity.com/paper/2004.14648/) (2020) - [Neural Cross-Lingual Coreference Resolution and its Application to Entity Linking](https://mlsanity.com/paper/1806.10201/) (2018) - [Document-level Relation Extraction with Context Guided Mention Integration and Inter-pair Reasoning](https://mlsanity.com/paper/2201.04826/) (2022) ## Built on by (most cited first, 19 of 19) - [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://mlsanity.com/paper/2410.01671/) (2024) - [The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works](https://mlsanity.com/paper/2510.15594/) (2025) - [BOOKCOREF: Coreference Resolution at Book Scale](https://mlsanity.com/paper/2507.12075/) (2025) - [Solving the Challenge Set without Solving the Task: On Winograd Schemas as a Test of Pronominal Coreference Resolution](https://mlsanity.com/paper/2410.09448/) (2024) - [Cross-Document Contextual Coreference Resolution in Knowledge Graphs](https://mlsanity.com/paper/2504.05767/) (2025) - [Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics](https://mlsanity.com/paper/2504.05855/) (2025) - [Multimodal Coreference Resolution for Chinese Social Media Dialogues: Dataset and Benchmark Approach](https://mlsanity.com/paper/2504.14321/) (2025) - [PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions](https://mlsanity.com/paper/2510.19060/) (2025) - [Adaptive Chunking: Optimizing Chunking-Method Selection for RAG](https://mlsanity.com/paper/2603.25333/) (2026) - [Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities](https://mlsanity.com/paper/2605.21369/) (2026) - [Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution](https://mlsanity.com/paper/2501.19316/) (2025) - [Coreference as an indicator of context scope in multimodal narrative](https://mlsanity.com/paper/2503.05298/) (2025) - [End-to-End Dialog Neural Coreference Resolution: Balancing Efficiency and Accuracy in Large-Scale Systems](https://mlsanity.com/paper/2504.05824/) (2025) - [Efficient Seq2seq Coreference Resolution Using Entity Representations](https://mlsanity.com/paper/2510.14504/) (2025) - [BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking](https://mlsanity.com/paper/2604.14389/) (2026) - [Interpretable Coreference Resolution Evaluation Using Explicit Semantics](https://mlsanity.com/paper/2605.10627/) (2026) - [Multilingual Coreference Resolution via Cycle-Consistent Machine Translation](https://mlsanity.com/paper/2606.05444/) (2026) - [NEST: Narrative Event Structures in Time for Long Video Understanding](https://mlsanity.com/paper/2606.19706/) (2026) - [A Circuit for Plural Reference: How LLMs Represent and Retrieve Singular and Plural Entities](https://mlsanity.com/paper/2609.03687/) (2026) ## Abstract (arXiv) 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.