# EDeR: A Dataset for Exploring Dependency Relations Between Events

arXiv: https://arxiv.org/abs/2304.01612 · HTML: https://mlsanity.com/paper/2304.01612/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 54 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [Meta-learning via Language Model In-context Tuning](https://mlsanity.com/paper/2110.07814/) (2021)
- [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://mlsanity.com/paper/2004.04494/) (2020)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [BARS: Towards Open Benchmarking for Recommender Systems](https://mlsanity.com/paper/2205.09626/) (2022)
- [The Causal News Corpus: Annotating Causal Relations in Event Sentences from News](https://mlsanity.com/paper/2204.11714/) (2022)
- [Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments](https://mlsanity.com/paper/2110.06865/) (2021)
- [Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning](https://mlsanity.com/paper/2202.13196/) (2022)
- [Towards Fine-grained Causal Reasoning and QA](https://mlsanity.com/paper/2204.07408/) (2022)
- [Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach](https://mlsanity.com/paper/2101.10766/) (2021)
- [Deep Bidirectional Transformers for Relation Extraction without Supervision](https://mlsanity.com/paper/1911.00313/) (2019)
- [Data Processing and Annotation Schemes for FinCausal Shared Task](https://mlsanity.com/paper/2012.02498/) (2020)
- [Refining Raw Sentence Representations for Textual Entailment Recognition via Attention](https://mlsanity.com/paper/1707.03103/) (2017)

## Abstract (arXiv)

Relation extraction is a central task in natural language processing (NLP) and information retrieval (IR) research. We argue that an important type of relation not explored in NLP or IR research to date is that of an event being an argument - required or optional - of another event. We introduce the human-annotated Event Dependency Relation dataset (EDeR) which provides this dependency relation. The annotation is done on a sample of documents from the OntoNotes dataset, which has the added benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for predicting the event dependency relation, the best of which achieves an accuracy of 82.61 for binary argument/non-argument classification. We show that recognizing this relation leads to more accurate event extraction (semantic role labelling) and can improve downstream tasks that depend on this, such as co-reference resolution. Furthermore, we demonstrate that predicting the three-way classification into the required argument, optional argument or non-argument is a more challenging task.