A system and method to interpret natural language requests and handle natural language responses in conversation is disclosed. The system includes an intent creation subsystem to receive one or more predefined intents to create one or more corresponding intent databases; a natural language message handling subsystem to receive a plurality of natural language messages from a user to identify one or more intents, to match one or more identified intents associated with the plurality of received natural language messages with the one or more predefined intents, handle the one or more identified intents by using a first message handling scheme when a similar match is found and a second message handling scheme in case of a dissimilar match; a natural language response handling subsystem to extract information from plurality of received natural language messages, to rectify the plurality of received natural language messages to handle a structured natural language response.

A system and method to interpret natural language requests and handle natural language responses in conversation is disclosed. The system includes an intent creation subsystem to receive one or more predefined intents to create one or more corresponding intent databases; a natural language message handling subsystem to receive a plurality of natural language messages from a user to identify one or more intents, to match one or more identified intents associated with the plurality of received natural language messages with the one or more predefined intents, handle the one or more identified intents by using a first message handling scheme when a similar match is found and a second message handling scheme in case of a dissimilar match; a natural language response handling subsystem to extract information from plurality of received natural language messages, to rectify the plurality of received natural language messages to handle a structured natural language response.

Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for an individual. A system configured as disclosed herein uses data from multiple meetings in which a user participated to create a user profile for the user. The system then receives data related to a new meeting in which the user participated, processes the new meeting data into segments using natural language processing, tags the resulting segments based on contexts, and compares the tagged segments to the user profile to generate a meeting effectiveness score for the new meeting which is specific to the user. The system can use machine learning to iteratively improve an ability of the system to generate the tagged segments using historical meeting data and updating that historical meeting data with each iteration of scoring a meeting's effectiveness.

Embodiments provide for improved data sequence validity processing, for example to determine validity of sentences or other language within a particular language domain. Such improved processing is useful at least for arranging data sequences based on determined validity, and/or making determinations and/or performing actions based on the determined validity. A determined probability (e.g., transformed into the perplexity space) of each token appearing in a data sequence is used in any of a myriad of manners to perform such data sequence validity processing. Example embodiments provide for generating a perplexity value set for each data sequence in a plurality of data sequences, generating a probabilistic ranking set for the plurality of data sequences based on the perplexity value sets and at least one sequence ranking metric, and generating an arrangement of the plurality of data sequences based on the probabilistic ranking set.

Embodiments provide for improved data sequence validity processing, for example to determine validity of sentences or other language within a particular language domain. Such improved processing is useful at least for arranging data sequences based on determined validity, and/or making determinations and/or performing actions based on the determined validity. A determined probability (e.g., transformed into the perplexity space) of each token appearing in a data sequence is used in any of a myriad of manners to perform such data sequence validity processing. Example embodiments provide for generating a perplexity value set for each data sequence in a plurality of data sequences, generating a probabilistic ranking set for the plurality of data sequences based on the perplexity value sets and at least one sequence ranking metric, and generating an arrangement of the plurality of data sequences based on the probabilistic ranking set.

Methods, systems, and computer program products for automated domain-specific constrained decoding from speech inputs to structured resources are provided herein. A computer-implemented method includes converting at least a portion of at least one user-provided speech utterance into text by processing the at least one user-provided speech utterance using an artificial intelligence-based automatic speech recognition model; automatically training an artificial intelligence-based decoding engine, wherein automatically training the artificial intelligence-based decoding engine comprising constraining the artificial intelligence-based decoding engine based at least in part on a domain-specific model and the artificial intelligence-based automatic speech recognition model; and generating at least one of one or more domain-specific text outputs related to one or more structured resources associated with the domain and one or more domain-specific action outputs related to the one or more structured resources associated with the domain by processing at least a portion of the text using the artificial intelligence-based decoding engine.

Provided is an end-to-end speech recognition technology capable of improving speech recognition performance in a desired specific domain, which includes collecting domain text data be specialized and comparing the data with a basic transcript text DB to determine domain text that is not included in the basic transcript text DB and requires additional training and constructing a specialization target domain text DB. The end-to-end speech recognition technology generates a speech signal from the domain text of the specialization target domain text DB, and trains a speech recognition neural network with the generated speech signal to generate an end-to-end speech recognition model specialized for the domain to be specialized. The specialized speech recognition model may be applied to the end-to-end speech recognizer to perform the domain-specific end-to-end speech recognition.

In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

A decoding method and apparatus in an artificial neural network for speech recognition. The decoding method in the artificial neural network for speech recognition includes performing a first decoding task of decoding a feature including speech information and at least one token recognized up to current time, using a shared decoding layer included in the artificial neural network, performing a second decoding task of decoding the at least one token, using the shared decoding layer, and determining an output token to be recognized subsequent to the at least one token based on a result of the first decoding task and a result of the second decoding task.