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Intent Detection systems in the real world are exposed to complexities ofimbalanced datasets containing varying perception of intent, unintendedcorrelations and domain-specific aberrations. To facilitate benchmarking whichcan reflect near real-world scenarios, we introduce 3 new datasets created fromlive chatbots in diverse domains. Unlike most existing datasets that arecrowdsourced, our datasets contain real user queries received by the chatbotsand facilitates penalising unwanted correlations grasped during the trainingprocess. We evaluate 4 NLU platforms and a BERT based classifier and find thatperformance saturates at inadequate levels on test sets because all systemslatch on to unintended patterns in training data.

HINT3: Raising the bar for Intent Detection in the Wild