Synthetic Data Generation and Multi-Task Learning for Extracting Temporal Information from Health-Related Narrative Text
Heereen Shim, Dietwig Lowet, Stijn Luca, Bart Vanrumste · 2021
Extracting temporal information is critical to process health-related text.Temporal information extraction is a challenging task for language models because it requires processing both texts and numbers.Moreover, the fundamental challenge is how to obtain a largescale training dataset.To address this, we propose a synthetic data generation algorithm.Also, we propose a novel multi-task temporal information extraction model and investigate whether multi-task learning can contribute to performance improvement by exploiting additional training signals with the existing training data.For experiments, we collected a custom dataset containing unstructured texts with temporal information of sleep-related activities.Experimental results show that utilising synthetic data can improve the performance when the augmentation factor is 3.The results also show that when multi-task learning is used with an appropriate amount of synthetic data, the performance can significantly improve from 82. to 88.6 and from 83.9 to 91.9 regarding micro-and macro-average exact match scores of normalised time prediction, respectively.