Systems and Methods for Implementing Smart Assistant Systems
In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.
1 . A method comprising, by one or more computing systems:
receiving, from a client system associated with a user, a user query;
generating, by an agent, an initial response for the user query;
determining, based on a machine-learning model, a confidence of the agent for generating the initial response;
modifying the initial response based on a comparison between the confidence of the agent and a factual correctness of the initial response; and
sending, to the client system, instructions for presenting the modified response.
2 . A method comprising, by one or more computing systems:
accessing a programming code generated by a user, wherein the programming code is associated with one or more user annotations, each user annotation comprising one or more of an entity, an action, or a parameter associated with a new language domain;
generating a structured representation based on an analysis of the one or more user annotations;
generating a natural-language understanding (NLU) ontology based on the structured representation; and
upon receiving a user utterance associated with the new domain, executing one or more tasks determined based on a comparison between the user utterance and the NLU ontology.
3 . A method comprising, by an assistant system:
accessing one or more example dialogues between one or more users and the assistant system;
inputting the example dialogues to a language model to generate a user turn;
generating, responsive to the user turn by the assistant system, a system response;
adding the user turn and the system response to the example dialogues; and
determining, based on the example dialogues, one or more metrics regarding natural-language conversation for the assistant system.
4 . A method comprising, by one or more computing systems:
accessing a plurality of utterances;
generating, based on a natural-language understanding (NLU) model, a plurality of embeddings for the plurality of utterances, respectively;
generating, based on one or more NLU rules, a plurality of intent or slot representations for the plurality of utterances, respectively; and
training a hybrid end-to-end model based on the plurality of embeddings, the plurality of intent or slot representation, dialog states associated with the plurality of utterances, and one or more dialog policies.
5 . A method comprising, by a client system:
receiving, at the client system, a first speech input from a user;
determining, a stateful convolution model, that the first speech input comprises a wake-word, wherein the wake-word is associated with a function of activating an assistant system, wherein the determining comprises:
processing the first speech input at a first time-step based on a first sliding window by the stateful convolution model, wherein the processing generates a first hidden state corresponding to information associated with the first time-step, and
inputting the first hidden state and a second sliding window to the stateful convolution model for processing the first speech input at a second time-step;
receiving, at the client system, a second speech input from the user; and
presenting, by the client system, a response generated by the assistant system, wherein the response corresponds to the second speech input.