Last year exposed all of us to a global uncertainty no one ever anticipated. It cost us billions of lives and posed new challenges for economies to survive. At the same time, it also highlighted the need for innovation as healthcare workers and other professionals stepped up to adapt to the new normal & selflessly help others.

Take a look at the video below, the solution in play is what we’ll be explaining through this write up. Notice the processing at the bottom of the screen grab to better understand the solution.

Intent Detection is a vital part of the Natural Language Understanding (NLU) pipeline of  Task-oriented dialogue systems. Recent advances in NLP have enabled systems that perform quite well on existing intent detection benchmarking datasets like HWU64, CLINC150, BANKING77 as shown in Larson et al., 2019, Casanueva et al., 2020. However, most existing datasets for intent detection are generated using crowdsourcing services. This difference in dataset preparation methodology leads to assumptions about training data which are no longer valid in the real world. In the real world, definition of intent often varies across users, tasks and domains.