公开(公告)号：US10380236B1

公开(公告)日：2019-08-13

申请号：US15712933

申请日：2017-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Hrishikesh Vidyadhar Ganu ,  Rajeev Ramnarain Rastogi ,  Subhajit Sanyal

IPC: G06F17/24 ,  G06N3/02 ,  G06F17/27 ,  G06F17/22

Abstract: Systems and methods are disclosed to implement a machine learning system that is trained to assign annotations to text fragments in an unstructured sequence of text. The system employs a neural model that includes an encoder recurrent neural network (RNN) and a decoder RNN. The input text sequence is encoded by the encoder RNN into successive encoder hidden states. The encoder hidden states are then decoded by the decoder RNN to produce a sequence of annotations for text fragments within the text sequence. In embodiments, the system employs a fixed-attention window during the decoding phase to focus on a subset of encoder hidden states to generate the annotations. In embodiments, the system employs a beam search technique to track a set of candidate annotation sequences before the annotations are outputted. By using a decoder RNN, the neural model is better equipped to capture long-range annotation dependencies in the text sequence.

公开(公告)号：US20240193420A1

公开(公告)日：2024-06-13

申请号：US18587662

申请日：2024-02-26

Applicant: Amazon Technologies, Inc.

Inventor： Arijit Biswas ,  Subhajit Sanyal

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and methods are disclosed to implement a neural network training system to train a multitask neural network (MNN) to generate a low-dimensional entity representation based on a sequence of events associated with the entity. In embodiments, an encoder is combined with a group of decoders to form a MNN to perform different machine learning tasks on entities. During training, the encoder takes a sequence of events in and generates a low-dimensional representation of the entity. The decoders then take the representation and perform different tasks to predict various attributes of the entity. As the MNN is trained to perform the different tasks, the encoder is also trained to generate entity representations that capture different attribute signals of the entities. The trained encoder may then be used to generate semantically meaningful entity representations for use with other machine learning systems.

公开(公告)号：US11941517B1

公开(公告)日：2024-03-26

申请号：US15821660

申请日：2017-11-22

Applicant: Amazon Technologies, Inc.

Inventor： Arijit Biswas ,  Subhajit Sanyal

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and methods are disclosed to implement a neural network training system to train a multitask neural network (MNN) to generate a low-dimensional entity representation based on a sequence of events associated with the entity. In embodiments, an encoder is combined with a group of decoders to form a MNN to perform different machine learning tasks on entities. During training, the encoder takes a sequence of events in and generates a low-dimensional representation of the entity. The decoders then take the representation and perform different tasks to predict various attributes of the entity. As the MNN is trained to perform the different tasks, the encoder is also trained to generate entity representations that capture different attribute signals of the entities. The trained encoder may then be used to generate semantically meaningful entity representations for use with other machine learning systems.