# Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

arXiv: https://arxiv.org/abs/2106.12384 · HTML: https://mlsanity.com/paper/2106.12384/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 23 Jun 2021.

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

- [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)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [Neural Belief Tracker: Data-Driven Dialogue State Tracking](https://mlsanity.com/paper/1606.03777/) (2016)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation](https://mlsanity.com/paper/1809.09078/) (2018)
- [Zero-Shot Transfer Learning for Event Extraction](https://mlsanity.com/paper/1707.01066/) (2017)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing](https://mlsanity.com/paper/1807.06517/) (2018)
- [Reading the Manual: Event Extraction as Definition Comprehension](https://mlsanity.com/paper/1912.01586/) (2019)
- [Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems](https://mlsanity.com/paper/1905.05644/) (2019)
- [Budgeted Policy Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1906.00499/) (2019)
- [Scale Up Event Extraction Learning via Automatic Training Data Generation](https://mlsanity.com/paper/1712.03665/) (2017)
- [Semi-Supervised Event Extraction with Paraphrase Clusters](https://mlsanity.com/paper/1808.08622/) (2018)
- [Biomedical Event Extraction with Hierarchical Knowledge Graphs](https://mlsanity.com/paper/2009.09335/) (2020)

## Built on by (most cited first, 10 of 10)

- [PAD-Net: An Efficient Framework for Dynamic Networks](https://mlsanity.com/paper/2211.05528/) (2022)
- [Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory](https://mlsanity.com/paper/2205.12179/) (2022)
- [Universal Information Extraction with Meta-Pretrained Self-Retrieval](https://mlsanity.com/paper/2306.10444/) (2023)
- [Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems](https://mlsanity.com/paper/2211.08168/) (2022)
- [Heterogeneous Social Event Detection via Hyperbolic Graph Representations](https://mlsanity.com/paper/2302.10362/) (2023)
- [LED: A Dataset for Life Event Extraction from Dialogs](https://mlsanity.com/paper/2304.08327/) (2023)
- [Document-Level Event Extraction with Definition-Driven ICL](https://mlsanity.com/paper/2408.05566/) (2024)
- [Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding](https://mlsanity.com/paper/2605.30742/) (2026)
- [Unbiased and Efficient Self-Supervised Incremental Contrastive Learning](https://mlsanity.com/paper/2301.12104/) (2023)
- [Survival Analysis on Structured Data using Deep Reinforcement Learning](https://mlsanity.com/paper/2205.14331/) (2022)

## Abstract (arXiv)

Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument's role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification.