# Coreference Resolution without Span Representations

arXiv: https://arxiv.org/abs/2101.00434 · HTML: https://mlsanity.com/paper/2101.00434/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 2 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [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)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [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)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [Revealing the Myth of Higher-Order Inference in Coreference Resolution](https://mlsanity.com/paper/2009.12013/) (2020)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://mlsanity.com/paper/2010.02807/) (2020)

## Built on by (most cited first, 30 of 45)

- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [Coreference Resolution through a seq2seq Transition-Based System](https://mlsanity.com/paper/2211.12142/) (2022)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022)
- [The NLP Task Effectiveness of Long-Range Transformers](https://mlsanity.com/paper/2202.07856/) (2022)
- [Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes](https://mlsanity.com/paper/2210.12197/) (2022)
- [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://mlsanity.com/paper/2407.21489/) (2024)
- [Findings of the Shared Task on Multilingual Coreference Resolution](https://mlsanity.com/paper/2209.07841/) (2022)
- [Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains](https://mlsanity.com/paper/2410.08327/) (2024)
- [Seq2seq is All You Need for Coreference Resolution](https://mlsanity.com/paper/2310.13774/) (2023)
- [End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline](https://mlsanity.com/paper/2107.01700/) (2021)
- [Mention Annotations Alone Enable Efficient Domain Adaptation for Coreference Resolution](https://mlsanity.com/paper/2210.07602/) (2022)
- [Examining risks of racial biases in NLP tools for child protective services](https://mlsanity.com/paper/2305.19409/) (2023)
- [CorPipe at CRAC 2024: Predicting Zero Mentions from Raw Text](https://mlsanity.com/paper/2410.02756/) (2024)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Longtonotes: OntoNotes with Longer Coreference Chains](https://mlsanity.com/paper/2210.03650/) (2022)
- [A Survey on Semantic Processing Techniques](https://mlsanity.com/paper/2310.18345/) (2023)
- [A Controlled Reevaluation of Coreference Resolution Models](https://mlsanity.com/paper/2404.00727/) (2024)
- [A Structured Span Selector](https://mlsanity.com/paper/2205.03977/) (2022)
- [Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution](https://mlsanity.com/paper/2305.15389/) (2023)
- [BOOKCOREF: Coreference Resolution at Book Scale](https://mlsanity.com/paper/2507.12075/) (2025)
- [An Empirical Study on Finding Spans](https://mlsanity.com/paper/2210.06824/) (2022)
- [Evaluating and Improving the Coreference Capabilities of Machine Translation Models](https://mlsanity.com/paper/2302.08464/) (2023)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Sentence-Incremental Neural Coreference Resolution](https://mlsanity.com/paper/2305.16947/) (2023)
- [Parallel Data Helps Neural Entity Coreference Resolution](https://mlsanity.com/paper/2305.17709/) (2023)
- [Multilingual Coreference Resolution in Low-resource South Asian Languages](https://mlsanity.com/paper/2402.13571/) (2024)

## Abstract (arXiv)

The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder. While highly effective, the model has a very large memory footprint -- primarily due to dynamically-constructed span and span-pair representations -- which hinders the processing of complete documents and the ability to train on multiple instances in a single batch. We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics. Our model performs competitively with the current standard model, while being simpler and more efficient.