CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction
This addresses the problem of extracting scattered arguments in documents for NLP applications, offering an incremental improvement over existing methods.
The paper tackles implicit event argument extraction by proposing a curriculum learning based prompt tuning approach that captures long-range dependencies and reduces reliance on labeled data, achieving state-of-the-art results on benchmark datasets in both fully-supervised and low-data scenarios.
Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document. Most previous work focuses on learning the direct relations between arguments and the given trigger, while the implicit relations with long-range dependency are not well studied. Moreover, recent neural network based approaches rely on a large amount of labeled data for training, which is unavailable due to the high labelling cost. In this paper, we propose a Curriculum learning based Prompt tuning (CUP) approach, which resolves implicit EAE by four learning stages. The stages are defined according to the relations with the trigger node in a semantic graph, which well captures the long-range dependency between arguments and the trigger. In addition, we integrate a prompt-based encoder-decoder model to elicit related knowledge from pre-trained language models (PLMs) in each stage, where the prompt templates are adapted with the learning progress to enhance the reasoning for arguments. Experimental results on two well-known benchmark datasets show the great advantages of our proposed approach. In particular, we outperform the state-of-the-art models in both fully-supervised and low-data scenarios.