Amazon Technologies, Inc.

Xiaofei Ma

Bing Xiang

Ramesh M Nallapati

Peng Xu

Sudipta Sengupta

Zhiguo Wang

Wei Xiao

Cicero Nogueira Dos Santos

Patrick Ng

Atul Deo

Andrew Oliver Arnold

Saurabh Kumar Singh

Dimitrios Soulios

Anuroop Arora

Henghui Zhu

Yong Yuan

Dejiao Zhang

Ankit Sultania

Krishnakumar Rajagopalan

Dong Wang

Zhiheng Huang

Jean-Pierre Dodel

Milan Saini

Kaustubh Kishor Khanke

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

Systems and methods for automated communication summarization

Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

Systems, apparatuses, and methods to generate synthetic queries from customer data for training of document querying machine learning models

Techniques for searching documents are described. An exemplary method includes receiving a document search query; querying at least one index based upon the document search query to identify matching data; fetching the identified matched data; determining one or more of a top ranked passage and top ranked documents from the set of documents based upon one or more invocations of one or more machine learning models based at least on the fetched identified matched data and the document search query; and returning one or more of the top ranked passage and the proper subset of documents.

Systems, apparatuses, and methods for document querying

Growing community of inventors

New York, NY, United States of America

Xiaofei Ma