# ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

arXiv: https://arxiv.org/abs/2309.12892 · HTML: https://mlsanity.com/paper/2309.12892/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [KoLA: Carefully Benchmarking World Knowledge of Large Language Models](https://mlsanity.com/paper/2306.09296/) (2023)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [How Far are We from Effective Context Modeling? An Exploratory Study on Semantic Parsing in Context](https://mlsanity.com/paper/2002.00652/) (2020)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [Multimodal Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/2011.08899/) (2020)
- [Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events](https://mlsanity.com/paper/1707.07344/) (2017)
- [ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification](https://mlsanity.com/paper/2204.07434/) (2022)
- [Semantic Structure Enhanced Event Causality Identification](https://mlsanity.com/paper/2305.12792/) (2023)
- [Question Answering as Global Reasoning over Semantic Abstractions](https://mlsanity.com/paper/1906.03672/) (2019)
- [Integrating Deep Event-Level and Script-Level Information for Script Event Prediction](https://mlsanity.com/paper/2110.15706/) (2021)
- [End-to-End Neural Event Coreference Resolution](https://mlsanity.com/paper/2009.08153/) (2020)
- [Pre-training to Match for Unified Low-shot Relation Extraction](https://mlsanity.com/paper/2203.12274/) (2022)
- [SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres](https://mlsanity.com/paper/2305.13617/) (2023)

## Built on by (most cited first, 5 of 5)

- [Graph is a Substrate Across Data Modalities](https://mlsanity.com/paper/2601.22384/) (2026)
- [Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering](https://mlsanity.com/paper/2410.04752/) (2024)
- [Are LLMs Good Annotators for Discourse-level Event Relation Extraction?](https://mlsanity.com/paper/2407.19568/) (2024)
- [EventFull: Complete and Consistent Event Relation Annotation](https://mlsanity.com/paper/2412.12733/) (2024)
- [MAQInstruct: Instruction-based Unified Event Relation Extraction](https://mlsanity.com/paper/2502.03954/) (2025)

## Abstract (arXiv)

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.