Deep reinforced model for abstractive summarization
Disclosed RNN-implemented methods and systems for abstractive text summarization
process input token embeddings of a document through an encoder that produces encoder …
process input token embeddings of a document through an encoder that produces encoder …
Deep neural network-based decision network
The technology disclosed proposes using a combination of computationally cheap, less-
accurate bag of words (BoW) model and computationally expensive, more-accurate long …
accurate bag of words (BoW) model and computationally expensive, more-accurate long …
Neural machine translation with latent tree attention
J Bradbury - US Patent 10,565,318, 2020 - Google Patents
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 …
translation that accomplishes the longstanding goal of natural language processing to take …
Memory grounded conversational reasoning and question answering for assistant systems
Natural-language Understanding, https://en. wikipedia. org/wiki/Natural-language
understanding, Feb. 15, 2018. Conversational AI and the Road Ahead, https://techcrunch …
understanding, Feb. 15, 2018. Conversational AI and the Road Ahead, https://techcrunch …
Sentinel gate for modulating auxiliary information in a long short-term memory (lstm) neural network
The technology disclosed presents a novel spatial attention model that uses current hidden
state information of a decoder long short-term memory (LSTM) to guide attention and to …
state information of a decoder long short-term memory (LSTM) to guide attention and to …
Generating dual sequence inferences using a neural network model
A computer-implemented method for dual sequence infer ence using a neural network
model includes generating a codependent representation based on a first input represen …
model includes generating a codependent representation based on a first input represen …
Training a joint many-task neural network model using successive regularization
The technology disclosed provides a so-called" joint many task neural network model” to
solve a variety of increasingly complex natural language processing (NLP) tasks using …
solve a variety of increasingly complex natural language processing (NLP) tasks using …
Sequence-to-sequence prediction using a neural network model
A method for sequence-to-sequence prediction using a neural network model includes
generating an encoded representation based on an input sequence using an encoder of the …
generating an encoded representation based on an input sequence using an encoder of the …
Spatial attention model for image captioning
The technology disclosed presents a novel spatial attention model that uses current hidden
state information of a decoder long short-term memory (LSTM) to guide attention and to …
state information of a decoder long short-term memory (LSTM) to guide attention and to …
Dynamic coattention network for question answering
The technology disclosed relates to an end-to-end neural network for question answering,
referred to herein as" dynamic coattention network (DCN)”. Roughly described, the DCN …
referred to herein as" dynamic coattention network (DCN)”. Roughly described, the DCN …