Domain Adaptation of SRL Systems for Biological Processes
Dheeraj Rajagopal, Nidhi Vyas, Aditya Siddhant, Anirudha Rayasam, Niket Tandon, Eduard H. Hovy · 2019
Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems.Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to lowresource domain-specific settings.In this paper, we propose two approaches for domain adaptation in biological domain that involve pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes.Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture.We perform our experiments on Pro-cessBank (Berant et al., 2014) dataset which contains less than 200 paragraphs on biological processes.We improve over the previous state-of-the-art system on this dataset by 21 F1 points.We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations.