Abstract: A computer-implemented method comprising: obtaining, by way of an input, input data relating to speech provided by a user; deriving one or more hypotheses for each of a plurality of user data fields from the input data; obtaining one or more reference values for each of the plurality of user data fields for each of one or more candidate users; calculating a score for at least one candidate user of the one or more candidate users, calculating the score comprising: calculating a plurality of user data field scores comprising, for each of the plurality of user data fields, a respective user data field score using the one or more hypotheses and the one or more reference values for the candidate user for the respective user data field; performing one or more fuzzy logic operations on the plurality of user data field scores; using the score for a candidate user of the one or more candidate users to perform a verification or identification process for the user.

Abstract: A method of obtaining a response to a query inputted by a user, the method comprising: receiving a user inputted query; representing the user inputted query as a sequence of embedding vectors using a first model; encoding the sequence of embedding vectors to produce a context vector using a second model; retrieving responses with associated response vectors; scoring response vectors against the context vector, wherein the scoring is a measure of the similarity between the context vector and a response vector; and outputting the responses with the closest response vectors, wherein the first model is configured to segment a user inputted query into a sequence of units from a vocabulary of units and represent each unit in the sequence as an embedding vector, wherein at least one of the units in the vocabulary is an incomplete word, and wherein the first model comprises parameters that are stored using eight bits per parameter; and wherein the second model has been trained using corresponding queries and response

Abstract: A computer-implemented method comprising: obtaining, by way of an input, input data relating to speech provided by a user; deriving one or more hypotheses for each of a plurality of user data fields from the input data; obtaining one or more reference values for each of the plurality of user data fields for each of one or more candidate users; calculating a score for at least one candidate user of the one or more candidate users, calculating the score comprising: calculating a plurality of user data field scores comprising, for each of the plurality of user data fields, a respective user data field score using the one or more hypotheses and the one or more reference values for the candidate user for the respective user data field; performing one or more fuzzy logic operations on the plurality of user data field scores; using the score for a candidate user of the one or more candidate users to perform a verification or identification process for the user.

Abstract: There is provided a dialogue system comprising: an input for obtaining an input signal relating to speech or text input provided by a user; an output for outputting speech or text information specified by a determined dialogue act; and a processor configured to: determine dialogue information from the input signal; determine the dialogue act based on the determined dialogue information, wherein determining the dialogue act comprises: selecting a next state from a plurality of states, wherein each of the plurality of states comprises information specifying a dialogue act and transition information specifying a transition to another state, the transitions defining one or more dialogue pathways, wherein selecting the next state comprises selecting a state which is specified by the transition information in a current state or selecting a state which is specified by a rule in a first set of one or more rules.

Abstract: A method of obtaining a response to a query inputted by a user, the method comprising: receiving a user inputted query; representing the user inputted query as a sequence of embedding vectors using a first model; encoding the sequence of embedding vectors to produce a context vector using a second model; retrieving responses with associated response vectors; scoring response vectors against the context vector, wherein the scoring is a measure of the similarity between the context vector and a response vector; and outputting the responses with the closest response vectors, wherein the first model is configured to segment a user inputted query into a sequence of units from a vocabulary of units and represent each unit in the sequence as an embedding vector, wherein at least one of the units in the vocabulary is an incomplete word, and wherein the first model comprises parameters that are stored using eight bits per parameter; and wherein the second model has been trained using corresponding queries and response

Abstract: A computer implemented method comprising: receiving input data relating to a speech or text signal originating from a user; representing the input data as a first sequence of first representations, each representing a unit of the input data; representing the input data as a second sequence of second representations, each representing one of the units of the input data; using a model to determine a tag sequence from the first sequence of first representations, wherein the model comprises an attention layer using the second sequence of second representations, wherein the tag sequence comprises one or more tags from a set of tags comprising a first tag; if one or more units of the input data correspond to the first tag, determining a system dialogue act based on the part of the input data corresponding to the first tag; and outputting speech or text information specified by the determined dialogue act.

Abstract: A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: generate features from the input signal; for each of a plurality of classifier models, each classifier model corresponding to a dialogue slot, and for one or more values corresponding to the dialogue slot, input features generated from the input signal, the classifier model outputting a probability corresponding to each of three or more relations, wherein the relations specify the relation of the value to the dialogue slot; update a belief state based on the outputs of the classifier models; determine a system dialogue act by inputting information relating to the belief state into a policy model; output speech or text information specified by the determined dialogue act at the output.

Abstract: A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: update a belief state, the belief state comprising information corresponding to one or more dialogue options, each dialogue option comprising a slot and a corresponding slot value, based on the input signal; determine a dialogue act, wherein a dialogue act is determined by applying one or more rules to world state information, the world state comprising information relating to the dialogue, wherein rules are applied in two or more ordered stages for each dialogue turn, wherein one of the stages is a first update stage, comprising applying one or more further rules controlling updating of the world state information based on the belief state information, and another of the stages is an act selection stage, comprising determining the dialogue act by applying the one or more rule

Abstract: A method of obtaining a response to a query inputted by a user, the method comprising: receiving a user inputted query; encoding said query to produce a context vector; retrieving responses with associated response vectors; scoring response vectors in the database against the context vector wherein the scoring is a measure of the similarity between the context vector and a response vector; and outputting the responses with the closest response vectors, wherein encoding said query to produce a context vector comprises using a pre-trained model, wherein said pre-trained model has been trained using corresponding queries and responses such that an encoding is used that maximises the similarity between the response vector and context vector for a corresponding query and response.

Abstract: A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: update a belief state, the belief state comprising information corresponding to one or more dialogue options, each dialogue option comprising a slot and a corresponding slot value, based on the input signal; determine a dialogue act, wherein a dialogue act is determined by applying one or more rules to world state information, the world state comprising information relating to the dialogue, wherein rules are applied in two or more ordered stages for each dialogue turn, wherein one of the stages is a first update stage, comprising applying one or more further rules controlling updating of the world state information based on the belief state information, and another of the stages is an act selection stage, comprising determining the dialogue act by applying the one or more rule

Abstract: A dialogue system comprising: an input for receiving input data relating to a speech or text signal originating from a user; an output for outputting speech or text information specified by a dialogue act; and a processor configured to: generate features from the input signal; for each of a plurality of classifier models, each classifier model corresponding to a dialogue slot, and for one or more values corresponding to the dialogue slot, input features generated from the input signal, the classifier model outputting a probability corresponding to each of three or more relations, wherein the relations specify the relation of the value to the dialogue slot; update a belief state based on the outputs of the classifier models; determine a system dialogue act by inputting information relating to the belief state into a policy model; output speech or text information specified by the determined dialogue act at the output.