Amazon Technologies, Inc.

Dejiao Zhang

Ramesh M Nallapati

Henghui Zhu

Bing Xiang

Atul Deo

Andrew Oliver Arnold

Anuroop Arora

Wei Xiao

Zhiguo Wang

Cicero Nogueira Dos Santos

Patrick Ng

Yifan Gao

Xiaofei Ma

Feng Nan

Kaustubh Kishor Khanke

Srikanth Prabala

Sai Bharadwaj Kanduri

Arvind Arikatla

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 predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Questions disambiguation using generative evidence fusion and round-trip prediction

Systems and methods to detect themes in contacts data. Contacts data may be encoded as text (e.g., chat logs), audio (e.g., audio recordings), and various other modalities. Text-based transcripts of contacts data may be parsed into turns, an issue turn may be detected using a machine learning model, a key phrase may be extracted from the issue turn. Key phrases from across multiple contacts data may be clustered to identify themes.

Programmatic theme detection in contacts analytics service

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Jersey City, NJ, United States of America

Dejiao Zhang