# Pairwise Representation Learning for Event Coreference

arXiv: https://arxiv.org/abs/2010.12808 · HTML: https://mlsanity.com/paper/2010.12808/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [Zero-Shot On-the-Fly Event Schema Induction](https://mlsanity.com/paper/2210.06254/) (2022)
- [Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles](https://mlsanity.com/paper/2404.08949/) (2024)
- [Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information](https://mlsanity.com/paper/2406.15990/) (2024)
- [A hybrid entity-centric approach to Persian pronoun resolution](https://mlsanity.com/paper/2211.06257/) (2022)
- [Link Prediction for Event Logs in the Process Industry](https://mlsanity.com/paper/2508.09096/) (2025)
- [Review of coreference resolution in English and Persian](https://mlsanity.com/paper/2211.04428/) (2022)
- [Argument-Centric Causal Intervention Method for Mitigating Bias in Cross-Document Event Coreference Resolution](https://mlsanity.com/paper/2506.01488/) (2025)
- [Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification](https://mlsanity.com/paper/2603.00621/) (2026)

## Abstract (arXiv)

Natural Language Processing tasks such as resolving the coreference of events require understanding the relations between two text snippets. These tasks are typically formulated as (binary) classification problems over independently induced representations of the text snippets. In this work, we develop a Pairwise Representation Learning (PairwiseRL) scheme for the event mention pairs, in which we jointly encode a pair of text snippets so that the representation of each mention in the pair is induced in the context of the other one. Furthermore, our representation supports a finer, structured representation of the text snippet to facilitate encoding events and their arguments. We show that PairwiseRL, despite its simplicity, outperforms the prior state-of-the-art event coreference systems on both cross-document and within-document event coreference benchmarks. We also conduct in-depth analysis in terms of the improvement and the limitation of pairwise representation so as to provide insights for future work.