Abstract As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most approaches to ATE broadly follow the traditional hybrid methodology, by first extracting list of unique candidate terms, and classifying these candidates based on predicted probability that they are valid terms. However, rise neural n...