Abstract: Methods and electronic devices for processing a spoken utterance associated with a user of an electronic device are disclosed. The method includes generating a textual representation of the spoken utterance having words, identifying a nonce word and a non-normalized word amongst the words, and generating a plurality of candidate textual representations based on the textual representation. The candidates have at least one of a first set of candidate textual representations and a second set of candidate textual representations, such that candidates from the first set are missing the nonce word from the words of the textual representation, and candidates from the second set have the non-normalized word from the words of the textual representation replaced by a normalized version thereof. The method includes comparing the candidates against grammars, and in response to a match, triggering an action associated with the grammar.

Abstract: There is disclosed a method and system for processing a user spoken utterance, the method comprising: receiving, from a user, an indication of the user spoken utterance; generating, a text representation hypothesis based on the user spoken utterance; processing, using a first trained scenario model and a second trained scenario model, the text representation hypothesis to generate a first scenario hypothesis and a second scenario hypothesis, respectively; the first trained scenario model and the second trained scenario model having been trained using at least partially different corpus of texts; analyzing, using a Machine Learning Algorithm (MLA), the first scenario hypothesis and the second scenario hypothesis to determine a winning scenario having a higher confidence score; based on the winning scenario, determining by an associated one of the first trained scenario model and the second trained scenario model, an action to be executed by the electronic device; executing the action.

Abstract: Methods and electronic devices for processing a spoken utterance associated with a user of an electronic device are disclosed. The method includes generating a textual representation of the spoken utterance having words, identifying a nonce word and a non-normalized word amongst the words, and generating a plurality of candidate textual representations based on the textual representation. The candidates have at least one of a first set of candidate textual representations and a second set of candidate textual representations, such that candidates from the first set are missing the nonce word from the words of the textual representation, and candidates from the second set have the non-normalized word from the words of the textual representation replaced by a normalized version thereof. The method includes comparing the candidates against grammars, and in response to a match, triggering an action associated with the grammar.

Abstract: Methods and servers for determining a training set for training a Machine Learning Algorithm (MLA) to perform classification of the digital objects are provided. A server acquires and orders training examples into an ordered sequence. A given training example has previous and subsequent examples in the ordered sequence. The server generates at least one of a textual feature and an embedding-based feature for the given example based on textual data and embedding-based data and the ground-truth classes, respectively, only in the previous training examples in the ordered sequence without taking into account the subsequent examples. The server determines the training set for the MLA having a training input and a label. The training input includes at least one of the textual feature and the embedding-based feature, and the label is representative of the ground-truth class of the respective object.

Abstract: There is disclosed a method and system for processing a user spoken utterance, the method comprising: receiving, from a user, an indication of the user spoken utterance; generating, a text representation hypothesis based on the user spoken utterance; processing, using a first trained scenario model and a second trained scenario model, the text representation hypothesis to generate a first scenario hypothesis and a second scenario hypothesis, respectively; the first trained scenario model and the second trained scenario model having been trained using at least partially different corpus of texts; analyzing, using a Machine Learning Algorithm (MLA), the first scenario hypothesis and the second scenario hypothesis to determine a winning scenario having a higher confidence score; based on the winning scenario, determining by an associated one of the first trained scenario model and the second trained scenario model, an action to be executed by the electronic device; executing the action.

Abstract: A method and computer device for selecting a current context-specific response for a current query is disclosed. The method comprises receiving a current context snippet comprising the current query; generating, by a neural network, a current context vector based on the current context snippet; for each response snippet associated with a respective one of a plurality of vectors, generating a respective ranking score as a value of (1) a respective first vector similarity value being indicative of a linguistic similarity between (i) the current context snippet and (ii) the respective context snippet, and (2) a respective second vector similarity value being indicative of a linguistic and a logical similarity between (i) the current context snippet and (ii) the respective response snippet. The method comprises selecting the current context-specific response amongst the response snippets based on the respective ranking scores.

Abstract: A method and computer device for selecting a current context-specific response for a current query is disclosed. The method comprises receiving a current context snippet comprising the current query; generating, by a neural network, a current context vector based on the current context snippet; for each response snippet associated with a respective one of a plurality of vectors, generating a respective ranking score as a value of (1) a respective first vector similarity value being indicative of a linguistic similarity between (i) the current context snippet and (ii) the respective context snippet, and (2) a respective second vector similarity value being indicative of a linguistic and a logical similarity between (i) the current context snippet and (ii) the respective response snippet. The method comprises selecting the current context-specific response amongst the response snippets based on the respective ranking scores.