# Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation

arXiv: https://arxiv.org/abs/2005.11355 · HTML: https://mlsanity.com/paper/2005.11355/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 13 of 13)

- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
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- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://mlsanity.com/paper/2210.02952/) (2022)
- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks](https://mlsanity.com/paper/2111.01340/) (2021)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains](https://mlsanity.com/paper/2203.10659/) (2022)
- [Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection](https://mlsanity.com/paper/2305.14163/) (2023)
- [Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes](https://mlsanity.com/paper/2209.09485/) (2022)
- [MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts](https://mlsanity.com/paper/2211.13896/) (2022)
- [Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction](https://mlsanity.com/paper/2601.02884/) (2026)
- [A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21885/) (2026)

## Abstract (arXiv)

We tackle the task of building supervised event trigger identification models which can generalize better across domains. Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. ADA uses adversarial training to construct representations that are predictive for trigger identification, but not predictive of the example's domain. It requires no labeled data from the target domain, making it completely unsupervised. Experiments with two domains (English literature and news) show that ADA leads to an average F1 score improvement of 3.9 on out-of-domain data. Our best performing model (BERT-A) reaches 44-49 F1 across both domains, using no labeled target data. Preliminary experiments reveal that finetuning on 1% labeled data, followed by self-training leads to substantial improvement, reaching 51.5 and 67.2 F1 on literature and news respectively.