# What happens before and after: Multi-Event Commonsense in Event Coreference Resolution

arXiv: https://arxiv.org/abs/2302.09715 · HTML: https://mlsanity.com/paper/2302.09715/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 57 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [Want To Reduce Labeling Cost? GPT-3 Can Help](https://mlsanity.com/paper/2108.13487/) (2021)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Not Enough Data? Deep Learning to the Rescue!](https://mlsanity.com/paper/1911.03118/) (2019)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Reframing Human-AI Collaboration for Generating Free-Text Explanations](https://mlsanity.com/paper/2112.08674/) (2021)
- [Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions](https://mlsanity.com/paper/2010.03205/) (2020)
- [Revisiting Joint Modeling of Cross-document Entity and Event Coreference Resolution](https://mlsanity.com/paper/1906.01753/) (2019)
- [DARE: Data Augmented Relation Extraction with GPT-2](https://mlsanity.com/paper/2004.13845/) (2020)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://mlsanity.com/paper/2004.13248/) (2020)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [Paragraph-level Commonsense Transformers with Recurrent Memory](https://mlsanity.com/paper/2010.01486/) (2020)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://mlsanity.com/paper/1707.07344/) (2017)
- [Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference](https://mlsanity.com/paper/2110.05362/) (2021)
- [Paraphrasing vs Coreferring: Two Sides of the Same Coin](https://mlsanity.com/paper/2004.14979/) (2020)
- [Realistic Evaluation Principles for Cross-document Coreference Resolution](https://mlsanity.com/paper/2106.04192/) (2021)
- [A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution](https://mlsanity.com/paper/1504.05929/) (2015)
- [Sequential Cross-Document Coreference Resolution](https://mlsanity.com/paper/2104.08413/) (2021)
- [A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution](https://mlsanity.com/paper/2104.01697/) (2021)
- [Enhancing Interpretable Clauses Semantically using Pretrained Word Representation](https://mlsanity.com/paper/2104.06901/) (2021)
- [STaCK: Sentence Ordering with Temporal Commonsense Knowledge](https://mlsanity.com/paper/2109.02247/) (2021)
- [What GPT Knows About Who is Who](https://mlsanity.com/paper/2205.07407/) (2022)

## Built on by (most cited first, 12 of 12)

- [ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://mlsanity.com/paper/2304.14827/) (2023)
- [Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing](https://mlsanity.com/paper/2407.11988/) (2024)
- [Any Other Thoughts, Hedgehog? Linking Deliberation Chains in Collaborative Dialogues](https://mlsanity.com/paper/2410.19301/) (2024)
- [Commonsense for Zero-Shot Natural Language Video Localization](https://mlsanity.com/paper/2312.17429/) (2023)
- [Multimodal Cross-Document Event Coreference Resolution Using Linear Semantic Transfer and Mixed-Modality Ensembles](https://mlsanity.com/paper/2404.08949/) (2024)
- [Do large language models and humans have similar behaviors in causal inference with script knowledge?](https://mlsanity.com/paper/2311.07311/) (2023)
- [Synergetic Event Understanding: A Collaborative Approach to Cross-Document Event Coreference Resolution with Large Language Models](https://mlsanity.com/paper/2406.02148/) (2024)
- [COMET-M: Reasoning about Multiple Events in Complex Sentences](https://mlsanity.com/paper/2305.14617/) (2023)
- [Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation](https://mlsanity.com/paper/2404.03196/) (2024)
- [Inferring Scientific Cross-Document Coreference and Hierarchy with Definition-Augmented Relational Reasoning](https://mlsanity.com/paper/2409.15113/) (2024)
- [EasyECR: A Library for Easy Implementation and Evaluation of Event Coreference Resolution Models](https://mlsanity.com/paper/2406.14106/) (2024)
- [Argument-Centric Causal Intervention Method for Mitigating Bias in Cross-Document Event Coreference Resolution](https://mlsanity.com/paper/2506.01488/) (2025)

## Abstract (arXiv)

Event coreference models cluster event mentions pertaining to the same real-world event. Recent models rely on contextualized representations to recognize coreference among lexically or contextually similar mentions. However, models typically fail to leverage commonsense inferences, which is particularly limiting for resolving lexically-divergent mentions. We propose a model that extends event mentions with temporal commonsense inferences. Given a complex sentence with multiple events, e.g., "The man killed his wife and got arrested", with the target event "arrested", our model generates plausible events that happen before the target event - such as "the police arrived", and after it, such as "he was sentenced". We show that incorporating such inferences into an existing event coreference model improves its performance, and we analyze the coreferences in which such temporal knowledge is required.