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Active learning for transformer models in direction query tagging

Correct understanding of direction queries is essential in map search for providing accurate direction related results, including routing, travel distance, travel time estimation, etc. Slot tagging is the process of recognizing and annotating query terms as entities such as source, destination, travel mode, travel distance, or travel time, so that downstream map search components can surface the expected result. Transformer-based models have achieved state-of-the-art performance on various language understanding tasks, including slot tagging. However, such models require either good quality labeled data for fine-tuning or large amount of labeled data for full training. Active learning provides a solution for improving training efficiency by selecting only a small amount of very informative queries for labelling. It is not yet clear, though, how to properly apply active learning for transformer-based language models. In this paper, we propose a novel active learning method designed specifically for transformer models and demonstrate its effectiveness for slot tagging of direction queries.