Few-shot Incremental Event Detection

TL;DR

This paper introduces a new task called few-shot incremental event detection, enabling models to learn new event classes with limited data while retaining previous knowledge, supported by a new benchmark dataset and improved results.

Contribution

The paper defines the novel task of few-shot incremental event detection, creates a benchmark dataset, and proposes methods that outperform baselines in this setting.

Findings

Higher F1-score than baseline methods

More stable detection performance

Effective learning with limited data

Abstract

Event detection tasks can enable the quick detection of events from texts and provide powerful support for downstream natural language processing tasks. Most such methods can only detect a fixed set of predefined event classes. To extend them to detect a new class without losing the ability to detect old classes requires costly retraining of the model from scratch. Incremental learning can effectively solve this problem, but it requires abundant data of new classes. In practice, however, the lack of high-quality labeled data of new event classes makes it difficult to obtain enough data for model training. To address the above mentioned issues, we define a new task, few-shot incremental event detection, which focuses on learning to detect a new event class with limited data, while retaining the ability to detect old classes to the extent possible. We created a benchmark dataset IFSED for the few-shot incremental event detection task based on FewEvent and propose two benchmarks, IFSED-K and IFSED-KP. Experimental results show that our approach has a higher F1-score than baseline methods and is more stable.