# On Event Individuation for Document-Level Information Extraction

arXiv: https://arxiv.org/abs/2212.09702 · HTML: https://mlsanity.com/paper/2212.09702/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [Question Answering is a Format; When is it Useful?](https://mlsanity.com/paper/1909.11291/) (2019)
- [Fine-Grained Temporal Relation Extraction](https://mlsanity.com/paper/1902.01390/) (2019)
- [Iterative Document-level Information Extraction via Imitation Learning](https://mlsanity.com/paper/2210.06600/) (2022)
- [The Universal Decompositional Semantics Dataset and Decomp Toolkit](https://mlsanity.com/paper/1909.13851/) (2019)
- [Automatic Error Analysis for Document-level Information Extraction](https://mlsanity.com/paper/2209.07442/) (2022)
- [Neural-Davidsonian Semantic Proto-role Labeling](https://mlsanity.com/paper/1804.07976/) (2018)
- [Decomposing and Recomposing Event Structure](https://mlsanity.com/paper/2103.10387/) (2021)
- [Asking the Right Questions in Low Resource Template Extraction](https://mlsanity.com/paper/2205.12643/) (2022)
- [Argument Linking: A Survey and Forecast](https://mlsanity.com/paper/2107.08523/) (2021)

## Built on by (most cited first, 2 of 2)

- [FAMuS: Frames Across Multiple Sources](https://mlsanity.com/paper/2311.05601/) (2023)
- [Are Triggers Needed for Document-Level Event Extraction?](https://mlsanity.com/paper/2411.08708/) (2024)

## Abstract (arXiv)

As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of event individuation -- the problem of distinguishing distinct events -- about which even human experts disagree. Through an annotation study and error analysis, we show that this raises concerns about the usefulness of template filling metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.