公开(公告)号：US20220414338A1

公开(公告)日：2022-12-29

申请号：US17361878

申请日：2021-06-29

Applicant: ADOBE INC.

Inventor： SANGWOO CHO ,  Franck Dernoncourt ,  Timothy Jeewun Ganter ,  Trung Huu Bui ,  Nedim Lipka ,  Varun Manjunatha ,  Walter Chang ,  Hailin Jin ,  Jonathan Brandt

IPC: G06F40/35 ,  G06F40/279

Abstract: System and methods for a text summarization system are described. In one example, a text summarization system receives an input utterance and determines whether the utterance should be included in a summary of the text. The text summarization system includes an embedding network, a convolution network, an encoding component, and a summary component. The embedding network generates a semantic embedding of an utterance. The convolution network generates a plurality of feature vectors based on the semantic embedding. The encoding component identifies a plurality of latent codes respectively corresponding to the plurality of feature vectors. The summary component identifies a prominent code among the latent codes and to select the utterance as a summary utterance based on the prominent code.

公开(公告)号：US20220277186A1

公开(公告)日：2022-09-01

申请号：US17186566

申请日：2021-02-26

Applicant: ADOBE INC.

Inventor： QUAN TRAN ,  Franck Dernoncourt ,  Walter Chang

IPC: G06N3/04 ,  G06N3/08 ,  G06F16/242

Abstract: The present disclosure describes systems and methods for dialog processing and information retrieval. Embodiments of the present disclosure provide a dialog system (e.g., a task-oriented dialog system) with adaptive recurrent hopping and dual context encoding to receive and understand a natural language query from a user, manage dialog based on natural language conversation, and generate natural language responses. For example, a memory network can employ a memory recurrent neural net layer and a decision meta network (e.g., a subnet) to determine an adaptive number of memory hops for obtaining readouts from a knowledge base. Further, in some embodiments, a memory network uses a dual context encoder to encode information from original context and canonical context using parallel encoding layers.

公开(公告)号：US20220179848A1

公开(公告)日：2022-06-09

申请号：US17116640

申请日：2020-12-09

Applicant: ADOBE INC.

Inventor： QUAN TRAN ,  Walter Chang ,  Franck Dernoncourt

IPC: G06F16/242 ,  G06N3/08 ,  H04L12/58 ,  G06F40/40

Abstract: The present disclosure provides a memory-based neural network for question answering. Embodiments of the disclosure identify meta-evidence nodes in an embedding space, where the meta-evidence nodes represent salient features of a training set. Each element of the training set may include a questions appended to a ground truth answer. The training set may also include questions with wrong answers that are indicated as such. In some examples, a neural Turing machine (NTM) reads a dataset and summarizes the dataset into a few meta-evidence nodes. A subsequent question may be appended to multiple candidate answers to form an input phrase, which may also be embedded in the embedding space. Then, corresponding weights may be identified for each of the meta-evidence nodes. The embedded input phrase and the weighted meta-evidence nodes may be used to identify the most appropriate answer.

公开(公告)号：US10909970B2

公开(公告)日：2021-02-02

申请号：US16135957

申请日：2018-09-19

Applicant: Adobe Inc.

Inventor： Seokhwan Kim ,  Walter Chang

IPC: G10L21/00 ,  G10L25/00 ,  G10L15/16 ,  G06F16/9032 ,  G10L15/22 ,  H04L12/58

Abstract: The present disclosure relates to generating digital responses based on digital dialog states generated by a neural network having a dynamic memory network architecture. For example, in one or more embodiments, the disclosed system provides a digital dialog having one or more segments to a dialog state tracking neural network having a dynamic memory network architecture that includes a set of multiple memory slots. In some embodiments, the dialog state tracking neural network further includes update gates and reset gates used in modifying the values stored in the memory slots. For instance, the disclosed system can utilize cross-slot interaction update/reset gates to accurately generate a digital dialog state for each of the segments of digital dialog. Subsequently, the system generates a digital response for each segment of digital dialog based on the digital dialog state.

公开(公告)号：US20210027141A1

公开(公告)日：2021-01-28

申请号：US16518894

申请日：2019-07-22

Applicant: Adobe Inc.

Inventor： Sean MacAvaney ,  Franck Dernoncourt ,  Walter Chang ,  Seokhwan Kim ,  Doo Soon Kim ,  Chen Fang

IPC: G06N3/04 ,  G06K9/62 ,  G06F17/16 ,  G06F17/15

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can classify term sequences within a source text based on textual features analyzed by both an implicit-class-recognition model and an explicit-class-recognition model. For example, by applying machine-learning models for both implicit and explicit class recognition, the disclosed systems can determine a class corresponding to a particular term sequence within a source text and identify the particular term sequence reflecting the class. The dual-model architecture can equip the disclosed systems to apply (i) the implicit-class-recognition model to recognize implicit references to a class in source texts and (ii) the explicit-class-recognition model to recognize explicit references to the same class in source texts.

公开(公告)号：US11967128B2

公开(公告)日：2024-04-23

申请号：US17333583

申请日：2021-05-28

Applicant: ADOBE INC.

Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter Chang

IPC: G06V10/56 ,  G06F16/51 ,  G06F16/532 ,  G06F16/56 ,  G06F16/583 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V10/56 ,  G06F16/51 ,  G06F16/532 ,  G06F16/56 ,  G06F16/5838 ,  G06V10/25 ,  G06V10/774 ,  G06V10/82 ,  G06T2207/20081

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

公开(公告)号：US11893345B2

公开(公告)日：2024-02-06

申请号：US17223166

申请日：2021-04-06

Applicant: ADOBE INC.

Inventor： Amir Pouran Ben Veyseh ,  Franck Dernoncourt ,  Quan Tran ,  Varun Manjunatha ,  Lidan Wang ,  Rajiv Jain ,  Doo Soon Kim ,  Walter Chang

IPC: G06F40/284 ,  G06F40/211 ,  G06F40/30 ,  G06N3/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06F40/284 ,  G06F40/126 ,  G06F40/211 ,  G06F40/30 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08

Abstract: Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a document comprising a plurality of words organized into a plurality of sentences, the words comprising an event trigger word and an argument candidate word, generate word representation vectors for the words, generate a plurality of document structures including a semantic structure for the document based on the word representation vectors, a syntax structure representing dependency relationships between the words, and a discourse structure representing discourse information of the document based on the plurality of sentences, generate a relationship representation vector based on the document structures, and predict a relationship between the event trigger word and the argument candidate word based on the relationship representation vector.

公开(公告)号：US11657802B2

公开(公告)日：2023-05-23

申请号：US17135629

申请日：2020-12-28

Applicant: Adobe Inc.

Inventor： Seokhwan Kim ,  Walter Chang

IPC: G10L21/00 ,  G10L25/00 ,  G10L15/16 ,  G06F16/9032 ,  G10L15/22 ,  H04L51/02

CPC classification number: G10L15/16 ,  G06F16/90332 ,  G10L15/22 ,  H04L51/02

Abstract: The present disclosure relates to generating digital responses based on digital dialog states generated by a neural network having a dynamic memory network architecture. For example, in one or more embodiments, the disclosed system provides a digital dialog having one or more segments to a dialog state tracking neural network having a dynamic memory network architecture that includes a set of multiple memory slots. In some embodiments, the dialog state tracking neural network further includes update gates and reset gates used in modifying the values stored in the memory slots. For instance, the disclosed system can utilize cross-slot interaction update/reset gates to accurately generate a digital dialog state for each of the segments of digital dialog. Subsequently, the system generates a digital response for each segment of digital dialog based on the digital dialog state.

公开(公告)号：US11620457B2

公开(公告)日：2023-04-04

申请号：US17177372

申请日：2021-02-17

Applicant: ADOBE INC.

Inventor： Logan Lebanoff ,  Franck Dernoncourt ,  Doo Soon Kim ,  Lidan Wang ,  Walter Chang

IPC: G06F40/40 ,  G06F40/284 ,  G06F40/166 ,  G06N3/04 ,  G06N3/08 ,  G06F40/30

Abstract: Systems and methods for sentence fusion are described. Embodiments receive coreference information for a first sentence and a second sentence, wherein the coreference information identifies entities associated with both a term of the first sentence and a term of the second sentence, apply an entity constraint to an attention head of a sentence fusion network, wherein the entity constraint limits attention weights of the attention head to terms that correspond to a same entity of the coreference information, and predict a fused sentence using the sentence fusion network based on the entity constraint, wherein the fused sentence combines information from the first sentence and the second sentence.

公开(公告)号：US20220383031A1

公开(公告)日：2022-12-01

申请号：US17333583

申请日：2021-05-28

Applicant: Adobe INC.

Inventor： Qiuyu Chen ,  Quan Hung Tran ,  Kushal Kafle ,  Trung Huu Bui ,  Franck Dernoncourt ,  Walter Chang

IPC: G06K9/46 ,  G06K9/32 ,  G06F16/51 ,  G06F16/583 ,  G06F16/532 ,  G06F16/56

Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.