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Building conversational systems in new domains and with added functionalityrequires resource-efficient models that work under low-data regimes (i.e., infew-shot setups). Motivated by these requirements, we introduce intentdetection methods backed by pretrained dual sentence encoders such as USE andConveRT. We demonstrate the usefulness and wide applicability of the proposedintent detectors, showing that: 1) they outperform intent detectors based onfine-tuning the full BERT-Large model or using BERT as a fixed black-boxencoder on three diverse intent detection data sets; 2) the gains areespecially pronounced in few-shot setups (i.e., with only 10 or 30 annotatedexamples per intent); 3) our intent detectors can be trained in a matter ofminutes on a single CPU; and 4) they are stable across different hyperparametersettings. In hope of facilitating and democratizing research focused onintention detection, we release our code, as well as a new challengingsingle-domain intent detection dataset comprising 13,083 annotated examplesover 77 intents.

Efficient Intent Detection with Dual Sentence Encoders