公开(公告)号：US20180300317A1

公开(公告)日：2018-10-18

申请号：US15901722

申请日：2018-02-21

Applicant: salesforce.com, inc.

Inventor： James BRADBURY

IPC: G06F17/28 ,  G06F17/27 ,  G06N3/04 ,  G06N3/08

Abstract: We introduce an attentional neural machine translation model for the task of machine translation that accomplishes the longstanding goal of natural language processing to take advantage of the hierarchical structure of language without a priori annotation. The model comprises a recurrent neural network grammar (RNNG) encoder with a novel attentional RNNG decoder and applies policy gradient reinforcement learning to induce unsupervised tree structures on both the source sequence and target sequence. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.

公开(公告)号：US20200184020A1

公开(公告)日：2020-06-11

申请号：US16264392

申请日：2019-01-31

Applicant: salesforce.com, inc.

Inventor： Kazuma HASHIMOTO ,  Raffaella BUSCHIAZZO ,  James BRADBURY ,  Teresa MARSHALL ,  Caiming XIONG ,  Richard SOCHER

IPC: G06F17/28

Abstract: Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

公开(公告)号：US20210103816A1

公开(公告)日：2021-04-08

申请号：US17122894

申请日：2020-12-15

Applicant: salesforce.com, inc.

Inventor： James BRADBURY ,  Stephen Joseph MERITY ,  Caiming XIONG ,  Richard SOCHER

IPC: G06N3/08 ,  G06N3/04 ,  G06F40/30 ,  G06F40/44 ,  G06F40/216 ,  G06F17/16 ,  G06N3/10

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

公开(公告)号：US20180129931A1

公开(公告)日：2018-05-10

申请号：US15420801

申请日：2017-01-31

Applicant: salesforce.com, inc.

Inventor： James BRADBURY ,  Stephen Joseph MERITY ,  Caiming XIONG ,  Richard SOCHER

IPC: G06N3/04

CPC classification number: G06N3/08 ,  G06F17/16 ,  G06F17/20 ,  G06F17/2715 ,  G06F17/2785 ,  G06F17/2818 ,  G06N3/04 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/10 ,  G10L15/16 ,  G10L15/18 ,  G10L15/1815 ,  G10L25/30

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

公开(公告)号：US20210216728A1

公开(公告)日：2021-07-15

申请号：US17214691

申请日：2021-03-26

Applicant: salesforce.com, inc.

Inventor： Kazuma HASHIMOTO ,  Raffaella BUSCHIAZZO ,  James BRADBURY ,  Teresa MARSHALL ,  Caiming XIONG ,  Richard SOCHER

IPC: G06F40/58

Abstract: Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

公开(公告)号：US20180082171A1

公开(公告)日：2018-03-22

申请号：US15421016

申请日：2017-01-31

Applicant: salesforce.com, inc.

Inventor： Stephen Joseph MERITY ,  Caiming XIONG ,  James BRADBURY ,  Richard SOCHER

IPC: G06N3/04 ,  G06N3/08 ,  G06N7/00 ,  G06F17/27

CPC classification number: G06N3/0445 ,  G06F17/277 ,  G06N3/0454 ,  G06N3/0472 ,  G06N3/08 ,  G06N3/084 ,  G06N7/005

Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.