The technology disclosed proposes using a combination of computationally cheap, less-
accurate bag of words (BoW) model and computationally expensive, more-accurate long …

A computer-implemented method for dual sequence inference using a neural network model
includes generating a codependent representation based on a first input representation of a …

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 …

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 …

The technology disclosed proposes using a combination of computationally cheap, less-
accurate bag of words (BoW) model and computationally expensive, more-accurate long …

US11222253B2 - Deep neural network model for processing data through multiple
linguistic task hierarchies - Google Patents US11222253B2 - Deep neural network model …

A system is provided for natural language processing. In some embodiments, the system
includes an encoder for generating context-specific word vectors for at least one input …

Approaches for multitask learning as question answering include a method for training that
includes receiving a plurality of training samples including training samples from a plurality …

Approaches for multitask learning as question answering include an input layer for encoding
a context and a question, a self-attention based transformer including an encoder and a …

A natural language processing system that includes a sen tence selector and a question
answering module. The sen tence selector receives a question and sentences that are …