Microsoft Technology Licensing, LLC

Yu Zhao

Weizhu Chen

Pengcheng He

Matthew Brigham Hall

Yuting Sun

Zheng Chen

Yi-Min Wang

Madhur Joshi

Brad Olenick

Alvaro Rahul Dias

Marck R Robinson

Eslam K Abdelreheem

Jiayuan Huang

Junyan Chen

Katherine Winant Osborne

Vikas Bahirwani

Julio Casal Terreros

Jade Huang

Vinela Pothineni

Wissam Zeidan

Methods, systems, apparatuses, and computer program products are provided for extracting an entity value from a sentence. An embedding set that may include one or more sentence embeddings is generated for at least part of a first sentence that is tagged to associate a first named entity in the sentence with an entity type. A plurality of candidate embeddings is also generated for at least part of a second sentence. The one or more sentence embeddings in the embedding set may be compared with each of the plurality of candidate embeddings, and a match score may be assigned to each comparison to generate a match score set. A particular match score of the match score set may be identified that exceeds a similarity threshold, and an entity value of the entity type may be extracted from the second sentence associated with the identified match score.

Example based entity extraction, slot filling and value recommendation

A system for training and deploying an artificial conversational entity using an artificial intelligence (AI) based communications system is disclosed. The system may comprise a memory storing machine readable instructions. The system may also comprise a processor to execute the machine readable instructions to receive a request via an artificial conversational entity. The processor may also transmit a response to the request based on a dialog tree generated from at least a model-based action generator and a memory-based action generator. The processor may further provide a training option to a user in the event the response is suboptimal. The processor may additionally receive a selection from the user via the training option. The selection may be associated with an optimal response.

Interactive dialog training and communication system using artificial intelligence

Computing systems, devices, and associated processes for distributed execution of extensions are disclosed herein. In one embodiment, a method includes receiving a user input at a client device for initiating an extension in a web page associated with a web site. In response to the received user input, a communication link between the client device and a remote computing facility can be established. The method can also include transmitting a request for initiating the extension, from the client device, to the remote computing facility via the established communication link and receiving, at the client device, data representing execution results of the requested extension from the remote computing facility.

Distributed extension execution in computing systems

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Redmond, WA, United States of America

Yu Zhao