Amazon Technologies, Inc.

Yaser Al-Onaizan

Yahor Pushkin

Sunil Mallya Kasaragod

Sravan Babu Bodapati

Rishita Rajal Anubhai

Miguel Ballesteros Martinez

Graham Vintcent Horwood

Sameer Karnik

Rama Krishna Sandeep Pokkunuri

Marcello Federico

Yogarshi Paritosh Vyas

Abhinav Goyal

Kalpit Dixit

Syed Kashif Hussain Shah

Georgiana Dinu

Jie Ma

Ravindra Manjunatha

Yinxiao Zhang

Swaminathan Sivasubramanian

Yi Zhang

Hassan Sawaf

Yusheng Xie

Andrew Paul Breen

Ganesh Kumar Gella

Xianglong Huang

Arvindh Krishnaswamy

Mehmet Umut Isik

Roger Scott Jenke

Jian Wang

Harshal Pimpalkhute

Roberto Barra-Chicote

Saman Zarandioon

Vasanth Philomin

Meghana Puvvadi

Dimitrios Soulios

Yuchen Tian

Robert Enyedi

Hyokun Yun

Prashant Mathur

Saab Mansour

Manikya Pavan Kiran Pothukuchi

Shuai Wang

Srikanth Doss Kadarundalagi Raghura

Kapil Singh Badesara

Santosh Kumar Ameti

Eric J Nowell

Aakash Upadhyay

Salvatore Romeo

Bo Pang

Diego Marcheggiani

Xuan Zhu

Liutong Zhou

Alok Kumar Parmesh

Aruni Roy Chowdhury

Anna Currey

Haiyu Yao

Alessandra Brusadin

Jonathan Steuck

Methods, systems, and computer-readable media for context-sensitive linking of entities to private databases are disclosed. An entity linking service stores a plurality of representations of entities. Individual ones of the entities correspond to individual ones of a plurality of records in one or more private databases. The entity linking service determines a mention of an entity in a document. The entity linking service selects, from the plurality of records in the one or more private databases, a record corresponding to the entity. The record is selected based at least in part on the plurality of representations of the entities and based at least in part on a context of the mention of the entity in the document. The entity linking service generates output comprising a reference to the selected record in the one or more private databases.

Context-sensitive linking of entities to private databases

Documents may be evaluated to determine inclusion of one or more types of information, such as personal information. Within the documents containing the one or more types of information, individual spans of information may be replaced with alternative data, which may maintain a type of content associated with the spans. Datasets of documents may be created where one or more types of information have been removed. These datasets may also be evaluated to determine whether one or more elements are over or under represented and then the datasets may be modified, such as by adding or removing documents, to provide an improved balance or distribution of the one or more elements.

Privacy preservation within datasets

A system receives one or more transcripts of communications between entities. The system identifies a requested action in the communications based at least in part on a mapping between the requested action and an application programming interface. The system identifies one or more statements eliciting information, based on parameters to the application programming interface. The system generates a definition of an artificial agent based, at least in part, on the requested action and the one more statements eliciting information.

Artificial agent generator

Methods, systems, and computer-readable media for event extraction from documents with co-reference are disclosed. An event extraction service identifies one or more trigger groups in a document comprising text. An individual one of the trigger groups comprises one or more textual references to an occurrence of an event. The one or more trigger groups are associated with one or more semantic roles for entities. The event extraction service identifies one or more entity groups in the document. An individual one of the entity groups comprises one or more textual references to a real-world object. The event extraction service assigns one or more of the entity groups to one or more of the semantic roles. The event extraction service generates an output indicating the one or more trigger groups and one or more entity groups assigned to the semantic roles.

Event extraction from documents with co-reference

Various embodiments of a hierarchical system or method of identifying sensitive content in data is described. In some embodiments, sensitive data classifiers local to a data storage system can analyze a plurality of data items and classify at least some data items as potentially containing sensitive data. The sensitive data classifiers can provide the classified data items to a separate sensitive data discovery component. The sensitive data discovery component can, in some embodiments, obtain the classified data items, perform a sensitive data location analysis on the classified data items to identify a location of sensitive data within some of the classified data items, and generate location information for the sensitive data within the data items containing sensitive data. The sensitive data discovery component can provide to a destination this information, in some embodiments, where the destination might redact, tokenize, highlight, or perform other actions on the located sensitive data.

Hierarchical system and method for identifying sensitive content in data

Techniques for performing natural language processing (NLP) on semi-structured data are described. An exemplary method includes receiving a semi-structured document to perform NLP on using a trained NLP model; converting the semi-structured document into a secondary format, wherein the secondary format includes spatial information for tokens of the semi-structured document; flattening the converted, secondary formatted semi-structured document into a Unicode Transformation Format text file; performing NLP on the Unicode Transformation Format text file using the trained NLP model; and providing a result of the NLP to a requester.

Natural language processing on semi-structured data

A translation system receives examples of translations between a first language and a second language. In response to receiving request to translate a source text from the first language to the second language, the system ranks the examples based on the example's applicability to one or more portions of the source text. The system performs additional training of a neural network that was pre-trained to translate from the first language to the second language, where the additional training is based on one or more top-ranking examples. The system translates the source text to the second language using the additionally trained neural network.

Machine translation with adapted neural networks

Techniques for creating a text classifier machine learning (ML) model are described. According to some embodiments, a language processing service finetunes a language ML model on unlabeled documents of a user, and then trains that finetuned language ML model on labeled documents of the user to be a text classifier that is customized for that user's domain, e.g., the user's documents. Additionally, the finetuned language ML model may be trained on labeled documents of the user, for prediction objectives for unlabeled data, before being trained as the text classifier.

Creating text classification machine learning models

Techniques for data lake-based text generation and data augmentation for machine learning training are described. A user-provided dataset including documents and corresponding label information can be automatically supplemented by creating additional high-quality document samples, with labels, via a large repository of documents in a data lake. Documents from the data lake may be identified as being semantically similar to the user-provided documents but different enough to allow a resulting model to learn from the variation in these documents. New documents can be generated from user-provided document samples or data lake sample documents by identifying and replacing slots within the samples and rewriting adjunct tokens.

Data lake-based text generation and data augmentation for machine learning training

Techniques for the generation of dubbed audio for an audio/video are described. An exemplary approach is to receive a request to generate dubbed speech for an audio/visual file; and in response to the request to: extract speech segments from an audio track of the audio/visual file associated with identified speakers; translate the extracted speech segments into a target language; determine a machine learning model per identified speaker, the trained machine learning models to be used to generate a spoken version of the translated, extracted speech segments based on the identified speaker; generate, per translated, extracted speech segment, a spoken version of the translated, extracted speech segments using a trained machine learning model that corresponds to the identified speaker of the translated, extracted speech segment and prosody information for the extracted speech segments; and replace the extracted speech segments from the audio track of the audio/visual file with the spoken versions spoken version of the translated, extracted speech segments to generate a modified audio track.

Multilingual speech translation with adaptive speech synthesis and adaptive physiognomy

Techniques for named-entity recognition are described. An exemplary implementation of a method includes extracting character features for each word of the document using a first encoder; extracting word level representations of for each word position using a second encoder, the word level representations being a concatenation of spelling variants; classifying the word level representations according to a first decoder; and outputting the classifications as named-entity labels.

Neural models for named-entity recognition

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Cortlandt Manor, NY, United States of America

Yaser Al-Onaizan