Abstract: An example embodiment may involve obtaining a chat dialog including a first question and a first action; obtaining, from a natural language model, an utterance based on the first question and an input parameter associated with the utterance; obtaining, from the natural language model, program code for performing the first action on a computing system, wherein the program code is based on a textual description of the first action and specification of a variable defined by the computing system in which to store the input parameter; and generating a virtual agent to perform the first action on the computing system using the program code.

Abstract: There is provided a non-transitory storage medium and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA). A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences.

Abstract: There is provided a non-transitory storage medium and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA). A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences.

Abstract: There is provided a method and a system for training an extractive machine learning algorithm (MLA) to generate extractive summaries of text documents. Reference documents and associated extractive summaries are received. The extractive MLA is then trained to generate an extractive summary, where the training includes, for a given reference document, encoding, using a sentence encoder, a plurality of reference sentences to obtain an associated plurality of sentence representations, encoding, using a document encoder, the associated plurality of sentence representations to obtain a document representation, extracting, using a decoder and based on the associated plurality of sentence representations and the document representation, a first reference sentence of the plurality of reference sentences to obtain a first extracted sentence. A given parameter is updated based on the first extracted sentence and the given reference document summary.

Abstract: There is provided a method and system for training and using a transformer language model (TLM) part of a recommendation engine. Natural language discussions about a category of items are received, the discussions comprising tags each indicative of a respective item belonging to the category of item. Information is received for each respective item. Based on the natural language discussions, the tags and the information about the respective item, the TLM is trained to: upon receipt of a user input, determine whether a given item should be recommended based on the user input, if the given item should be recommended, retrieving given information about the given item and generating a response to the user input, the response to the user input comprising the given item to be recommended and the given information, and output the response to the user input. The response is generated in natural language format.

Abstract: There is provided a method and a system for training an extractive machine learning algorithm (MLA) to generate extractive summaries of text documents. Reference documents and associated extractive summaries are received. The extractive MLA is then trained to generate an extractive summary, where the training includes, for a given reference document, encoding, using a sentence encoder, a plurality of reference sentences to obtain an associated plurality of sentence representations, encoding, using a document encoder, the associated plurality of sentence representations to obtain a document representation, extracting, using a decoder and based on the associated plurality of sentence representations and the document representation, a first reference sentence of the plurality of reference sentences to obtain a first extracted sentence. A given parameter is updated based on the first extracted sentence and the given reference document summary.

Abstract: A method and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA) and a method and a system for training the abstractive MLA. A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences. In some aspects, the extractive summary is generated by an extractive MLA having been trained to generate extractive summaries.

Abstract: A method and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA) and a method and a system for training the abstractive MLA. A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences. In some aspects, the extractive summary is generated by an extractive MLA having been trained to generate extractive summaries.

Abstract: Arbitrary input sounds are analyzed and the coefficients of a low-dimensional representation, such as LPC or MFCC, are determined as a measure of the timbre of the sounds. The coefficients can be employed in different ways to control output events, such as the generation of synthesized sounds. In one approach, the individual coefficients are mapped to the control parameters of a sound synthesizer, to enable highly complex output sounds to be generated in response to simple input sounds. In another approach, pattern recognition techniques are employed with respect to the coefficients, to classify the input sounds. Each classification is mapped to a control parameter, to cause different events to occur in response to the respective input sounds. In one example, the sounds of different musical instruments are generated in dependence upon the classification of the input sounds. These analysis techniques have low latency, and thereby allow events to be controlled without perceptible delay.