NLU datasets accelerating Conversational AI progress

Lack of training data for various tasks related to conversational AI, has been a bottleneck in its progress & adoption. Slot-filling bots are too fragile to stand the test of the time, have shown glaring deficiencies which are tough to plug. Natural conversation requires more than just intent detection and entity extraction which most of the chatbots rely on; lacking the key elements of NLU(syntactic, semantic, pragmatic) capabilities because of lack of good quality training data. Creating, annotating, synthesising large datasets with quality & quantity good enough to build such capabilities, are expensive,time consuming and requires skilled data annotators. You may be in luck now, if you're looking to build such systems because of some recent data set releases, which should help democratize conversational AI, with the power of open data.