Amazon Technologies, Inc.

Henghui Zhu

Ramesh M Nallapati

Bing Xiang

Dejiao Zhang

Zhiguo Wang

Patrick Ng

Atul Deo

Andrew Oliver Arnold

Anuroop Arora

Wei Xiao

Sudipta Sengupta

Stephen Michael Ash

Cicero Nogueira Dos Santos

Gregory David Adams

Xiaofei Ma

Yifan Gao

Anuj Chauhan

Sai Bharadwaj Kanduri

Arvind Arikatla

Feng Nan

Wuwei Lan

Kaustubh Kishor Khanke

Srikanth Prabala

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 handling natural language query processing are described. In some examples, a sequence-to-sequence model is used to handle a natural language query. Post-processing of a result of the sequence-to-sequence model utilizes fine-grained information from an entity linker. In some examples, the sequence-to-sequence model and aspects of a natural language query pipeline are used to handle a natural language query.

Natural language query processing

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

Henghui Zhu