Amazon Technologies, Inc.

Patrick Ng

Bing Xiang

Zhiguo Wang

Ramesh M Nallapati

Sudipta Sengupta

Stephen Michael Ash

Timothy Jones

Sharanabasappa Parashuram Revadigar

Hanbo Li

Gregory David Adams

Rishav Chakravarti

Jiarong Jiang

Mukul Vijay Karnik

Cicero Nogueira Dos Santos

Douglas Scott Cerny

Nanyan Li

Yung Haw Wang

Henghui Zhu

Jun Wang

Stephanie Weeks

Donald Harold Rivers Weidner

Deepak Shantha Murthy

Anuj Chauhan

Peng Xu

Andrew Oliver Arnold

Yifan Gao

Dejiao Zhang

Xiaofei Ma

Feng Nan

Wuwei Lan

Sheng Zhang

Techniques for handling natural language query processing are described. In some examples, entities are recognized during an entity recognition phase and then relations between those entities are determined. Those relations are fed to an entity linker to help the linker link candidate to columns and/or a intent representation generator to help parse multiple values and column pairs of a natural language query.

Natural language query processing

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.

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

Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Interactive assistance for executing natural language queries to data sets

Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Multiple stage filtering for natural language query processing pipelines

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

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Patrick Ng