Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters
Nghia Ngo Trung, Duy Phung, Thien Huu Nguyen · 2021
Due to the multi-dimensional variation of textual data, detection of event triggers from new domains can become a lot more challenging.This prompts a need to research on domain adaptation methods for event detection task, especially for the most practical unsupervised setting.Recently, large transformer-based language models, e.g.BERT, have become essential to achieve top performance for event detection.However, their unwieldy nature also prevents effective adaptation across domains.To this end, this work proposes a Domain-specific Adapter-based Adaptation (DAA) framework to improve the adaptability of BERT-based models for event detection across domains.By explicitly representing data from different domains with separate adapter modules in each layer of BERT, DAA introduces a novel joint representation learning mechanism and a Wasserstein distance-based technique for data selection in adversarial learning to substantially boost the performance on target domains.Extensive experiments and analysis over different datasets (i.e., LitBank, TimeBank, and ACE-05) demonstrate the effectiveness of our approach.