Amazon Technologies, Inc.

Saab Mansour

Yahor Pushkin

Yaser Al-Onaizan

Swaminathan Sivasubramanian

Yi Zhang

Rama Krishna Sandeep Pokkunuri

Ganesh Kumar Gella

Rishita Rajal Anubhai

Roger Scott Jenke

Harshal Pimpalkhute

Vasanth Philomin

Meghana Puvvadi

Kalpit Dixit

Manikya Pavan Kiran Pothukuchi

Santosh Kumar Ameti

Salvatore Romeo

A plurality of concepts is identified from utterances of a user of a service using a set of machine learning models. The plurality of concepts includes a primary concept representing an offering included in a catalog of the service, and a non-primary concept which provides additional information about the offering. A logical relationship is inferred among a pair of concepts using the set of machine learning models. A service order for the service is populated based at least in part on the relationship.

Machine learning techniques for dialog-based service order acquisition

A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

Artificial agent generator

A chat input identifier may receive various chat inputs based on voice or text inputs from a user. The chat input identifier may apply different filters to the chat inputs to identify one or more chat correction pairs (e.g., chat input with errors, corrected chat input) from among the plurality of chat inputs. The chat correction pairs are used to train an auto-correction model. The trained auto-correction model receives a given chat input that has one or more errors. The auto-correction model processes the given chat input to generate a corrected version of the given chat input (without the need to obtain a correction from the user). The corrected chat input is then provided to a dialog-driven application.

Identifying chat correction pairs for training model to automatically correct chat inputs

Identifying chat correction pairs for training models to automatically correct chat inputs

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Saab Mansour