# Generating Harder Cross-document Event Coreference Resolution Datasets using Metaphoric Paraphrasing

arXiv: https://arxiv.org/abs/2407.11988 · HTML: https://mlsanity.com/paper/2407.11988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 52 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [FLUTE: Figurative Language Understanding through Textual Explanations](https://mlsanity.com/paper/2205.12404/) (2022)
- [MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding](https://mlsanity.com/paper/2103.06779/) (2021)
- [I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors](https://mlsanity.com/paper/2305.14724/) (2023)
- [Cross-document Coreference Resolution over Predicted Mentions](https://mlsanity.com/paper/2106.01210/) (2021)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia](https://mlsanity.com/paper/2104.05022/) (2021)
- [Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization](https://mlsanity.com/paper/1805.10985/) (2018)
- [FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning](https://mlsanity.com/paper/2302.04834/) (2023)
- [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)
- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [Sequential Cross-Document Coreference Resolution](https://mlsanity.com/paper/2104.08413/) (2021)
- [What happens before and after: Multi-Event Commonsense in Event Coreference Resolution](https://mlsanity.com/paper/2302.09715/) (2023)
- [Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection](https://mlsanity.com/paper/1906.06703/) (2019)
- [Metaphoric Paraphrase Generation](https://mlsanity.com/paper/2002.12854/) (2020)
- [Linear Cross-document Event Coreference Resolution with X-AMR](https://mlsanity.com/paper/2404.08656/) (2024)
- [How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?](https://mlsanity.com/paper/2306.05434/) (2023)
- [COMET-M: Reasoning about Multiple Events in Complex Sentences](https://mlsanity.com/paper/2305.14617/) (2023)

## Built on by (most cited first, 6 of 6)

- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators](https://mlsanity.com/paper/2503.06778/) (2025)
- [Cross-Document Contextual Coreference Resolution in Knowledge Graphs](https://mlsanity.com/paper/2504.05767/) (2025)
- [Enhancing Coreference Resolution with Pretrained Language Models: Bridging the Gap Between Syntax and Semantics](https://mlsanity.com/paper/2504.05855/) (2025)
- [Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference](https://mlsanity.com/paper/2602.17424/) (2026)
- [Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification](https://mlsanity.com/paper/2603.00621/) (2026)

## Abstract (arXiv)

The most popular Cross-Document Event Coreference Resolution (CDEC) datasets fail to convey the true difficulty of the task, due to the lack of lexical diversity between coreferring event triggers (words or phrases that refer to an event). Furthermore, there is a dearth of event datasets for figurative language, limiting a crucial avenue of research in event comprehension. We address these two issues by introducing ECB+META, a lexically rich variant of Event Coref Bank Plus (ECB+) for CDEC on symbolic and metaphoric language. We use ChatGPT as a tool for the metaphoric transformation of sentences in the documents of ECB+, then tag the original event triggers in the transformed sentences in a semi-automated manner. In this way, we avoid the re-annotation of expensive coreference links. We present results that show existing methods that work well on ECB+ struggle with ECB+META, thereby paving the way for CDEC research on a much more challenging dataset. Code/data: https://github.com/ahmeshaf/llms_coref