公开(公告)号：US12038960B2

公开(公告)日：2024-07-16

申请号：US17528901

申请日：2021-11-17

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon

IPC: G06F16/35 ,  G06F18/214 ,  G06N3/04 ,  G06V30/416

CPC classification number: G06F16/35 ,  G06F18/214 ,  G06N3/04 ,  G06V30/416 ,  G06F2218/12

Abstract: An incongruent headline detection system receives a request to determine a headline incongruence score for an electronic document. The incongruent headline detection system determines the headline incongruence score for the electronic document by applying a machine learning model to the electronic document. Applying the machine learning model to the electronic document includes generating a graph representing a textual similarity between a headline of the electronic document and each of a plurality of paragraphs of the electronic document and determining the headline incongruence score using the graph. The incongruent headline detection system transmits, responsive to the request, the headline incongruence score for the electronic document.

公开(公告)号：US20230136527A1

公开(公告)日：2023-05-04

申请号：US17453562

申请日：2021-11-04

Applicant: ADOBE INC.

Inventor： Jianguo Zhang ,  Trung Huu Bui ,  Seunghyun Yoon ,  Xiang Chen ,  Quan Hung Tran ,  Walter W. Chang

IPC: G06F40/40 ,  G06F40/30 ,  G06F40/284 ,  G06V30/19

Abstract: Systems and methods for natural language processing are described. One or more aspects of a method, apparatus, and non-transitory computer readable medium include receiving a text phrase; encoding the text phrase using an encoder to obtain a hidden representation of the text phrase, wherein the encoder is trained during a first training phrase using self-supervised learning based on a first contrastive loss and during a second training phrase using supervised learning based on a second contrastive learning loss; identifying an intent of the text phrase from a predetermined set of intent labels using a classification network, wherein the classification network is jointly trained with the encoder in the second training phase; and generating a response to the text phrase based on the intent.

公开(公告)号：US11271876B2

公开(公告)日：2022-03-08

申请号：US16548140

申请日：2019-08-22

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Franck Dernoncourt ,  Doo Soon Kim ,  Trung Bui

IPC: H04L12/58 ,  G06F16/903 ,  G06F16/901 ,  G06N3/08 ,  H04L51/02

Abstract: The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.

公开(公告)号：US11205444B2

公开(公告)日：2021-12-21

申请号：US16543342

申请日：2019-08-16

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Subhadeep Dey ,  Seunghyun Yoon

IPC: G10L25/00 ,  G10L25/63 ,  G06N3/04 ,  G06F17/18 ,  G06F17/16 ,  G10L25/30

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining speech emotion. In particular, a speech emotion recognition system generates an audio feature vector and a textual feature vector for a sequence of words. Further, the speech emotion recognition system utilizes a neural attention mechanism that intelligently blends together the audio feature vector and the textual feature vector to generate attention output. Using the attention output, which includes consideration of both audio and text modalities for speech corresponding to the sequence of words, the speech emotion recognition system can apply attention methods to one of the feature vectors to generate a hidden feature vector. Based on the hidden feature vector, the speech emotion recognition system can generate a speech emotion probability distribution of emotions among a group of candidate emotions, and then select one of the candidate emotions as corresponding to the sequence of words.

公开(公告)号：US20210058345A1

公开(公告)日：2021-02-25

申请号：US16548140

申请日：2019-08-22

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Franck Dernoncourt ,  Doo Soon Kim ,  Trung Bui

IPC: H04L12/58 ,  G06N3/08 ,  G06F16/901 ,  G06F16/903

Abstract: The present disclosure relates to utilizing a graph neural network to accurately and flexibly identify text phrases that are relevant for responding to a query. For example, the disclosed systems can generate a graph topology having a plurality of nodes that correspond to a plurality of text phrases and a query. The disclosed systems can then utilize a graph neural network to analyze the graph topology, iteratively propagating and updating node representations corresponding to the plurality of nodes, in order to identify text phrases that can be used to respond to the query. In some embodiments, the disclosed systems can then generate a digital response to the query based on the identified text phrases.