Amazon Technologies, Inc.

Jared Kramer

Li Zhou

Kevin Michael Small

Ariel Grob Dos Santos

Jingxiang Chen

Vikas Seshagiri Rao Bhardwaj

Sheng Bi

Gunashekar Jayaraman

Oleg Rokhlenko

Tarun Agarwal

Polly Po Yee Lee

Hung Chiu Phillip Keung

Kapil Chahal

Jason Sun

Simi Wang

Dhvani Yogesh Shah

Anirban Chakraborty

Devin Didericksen

Hebatallah Elfardy

Andrea Kahn

A pipeline is provided for management of a pool of chat message templates for an automated dialog system. The pool of chat messages may be managed using machine learning-based clustering and feedback-based modifications. A set of chat messages may be analyzed using a machine learning model to generate different clusters of messages that are semantically related. Representative messages may be selected from each cluster and used in chat sessions according to the semantic context of the chat sessions. Based on feedback obtained during the chat sessions, metrics generated based on the feedback, and/or other data, modifications may be made to the clusters and/or the representative messages to improve the performance of the automated dialog system.

Architecture for dynamic management of dialog message templates

Techniques for simulating a user in a conversation are described. A user simulation service and a conversation agent service conduct a dialog. The user simulation service compares a current sequence of stored labels corresponding to statements in the dialog with a plurality of candidate sequences of labels corresponding to statements in a plurality of candidate dialogs to identify a matching sequence of labels. The user simulation sequence identifies a base sequence of labels that includes the matching sequence of labels to identify a label corresponding to an act in the base sequence of labels following the matching sequence of labels. The user simulation service issues the act to the conversation agent service to be used as a simulated user act.

Collaborative-filtering based user simulation for dialog systems

Disclosed are various embodiments for an automated service agent. Natural language inputs are obtained from a client. The natural language inputs and other data are input to machine learning models to identify intents reflected in the natural language inputs. A state in a workflow is updated to reflect the identified intents. Responses may be communicated to the client to further progress in the workflow.

Language based solution agent

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Seattle, WA, United States of America

Jared Kramer