---
title: "The Referential Reader — Liu 2019"
description: "Most coreference systems use the mention-pair approach, which scores every pair of spans."
authors: ["Fei Liu", "Luke Zettlemoyer", "Jacob Eisenstein"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1593/"
doi: "10.18653/v1/p19-1593"
arxiv: "1902.01541"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/liu-2019-referential-reader-recurrent-entity-network/"
---
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# The Referential Reader: A Recurrent Entity Network for Anaphora Resolution

**Authors:** [Fei Liu](https://md.research.elamur.ai/free/authors/fei-liu/), [Luke Zettlemoyer](https://md.research.elamur.ai/free/authors/luke-zettlemoyer/), [Jacob Eisenstein](https://md.research.elamur.ai/free/authors/jacob-eisenstein/) · **Year:** 2019 · **Venue:** Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P19-1593/) · [PDF](https://aclanthology.org/P19-1593.pdf) · [DOI 10.18653/v1/p19-1593](https://doi.org/10.18653/v1/p19-1593) · [arXiv 1902.01541](https://arxiv.org/abs/1902.01541) · [OpenAlex](https://openalex.org/W2964093485)

**Citations:** 18 citations (openalex-snapshot · 2026-10-14) · [13](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Most coreference systems use the mention-pair approach, which scores every pair of spans. This costs a lot of computation and annotated data, and it does not match how people read, which is roughly word by word. The authors propose the Referential Reader, which processes text strictly left to right and keeps a small fixed set of memory cells for entities. At each token, learned gates decide whether to update a cell (marking coreference with what it already holds), overwrite the least salient cell with a new entity, or skip the token. Salience grows with reuse and fades over time. Because all memory operations are differentiable, the model trains end-to-end on gold pronoun–name links. Its recurrent state also allows next-word prediction, so it can be pretrained as a language model on unlabeled text. On the GAP benchmark, the incremental model beats strong mention-pair and rule-based baselines. This shows that online, memory-based anaphora resolution is practical and can benefit from semi-supervised learning.

## Key points
- **Evaluation and pretraining data:** Evaluated on GAP (Webster et al., 2018) with the official script. Language-model pretraining used a 3.8M-token corpus built from the Wikipedia articles behind GAP instances.
- **Main results (two memory cells, strictly incremental):** LM + coref training reaches F1 72.1, with F1 M 72.8, F1 F 71.4 and bias 0.98. This exceeds retrained Lee et al. (2017) at 67.0 and Parallelism+URL at 70.6.
- **Effect of pretraining and BERT:** LM pretraining adds 3.2 F1 over coref-only training (68.9). Adding BERT embeddings raises F1 to 78.8, averaged over 5 seeds, but bias shifts slightly to 0.96.
- **Robustness tests:** On a test set made by concatenating pairs of GAP instances, F1 drops only to 70.2, so the model reuses its two cells effectively. Using four cells gives F1 71.4 on the original test set.
- **Implementation details:** Gumbel-softmax makes the choice of cell to overwrite differentiable. Salience half-lives are 4 entity mentions and 30 non-entity tokens. Code is available at github.com/liufly/refreader.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cited by

Cited by

13 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 10 not yet.

- [BERT for Coreference Resolution: Baselines and Analysis](https://md.research.elamur.ai/free/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
- [WikiCREM: A Large Unsupervised Corpus for Coreference Resolution](https://md.research.elamur.ai/free/papers/kocijan-2019-wikicrem-large-unsupervised-corpus-coreference/)
- [Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks](https://md.research.elamur.ai/free/papers/toshniwal-2020-learning-ignore-long-document-coreference/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{liu2019referential,
  title = {{The Referential Reader: A Recurrent Entity Network for Anaphora Resolution}},
  author = {Liu, Fei and Zettlemoyer, Luke and Eisenstein, Jacob},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1593},
  eprint = {1902.01541},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p19-1593}
}
```

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