公开(公告)号：US11755570B2

公开(公告)日：2023-09-12

申请号：US17116640

申请日：2020-12-09

Applicant: ADOBE INC.

Inventor： Quan Tran ,  Walter Chang ,  Franck Dernoncourt

IPC: G06F7/00 ,  G06F16/242 ,  G06F40/40 ,  H04L51/02 ,  G06N3/08

CPC classification number: G06F16/243 ,  G06F40/40 ,  G06N3/08 ,  H04L51/02

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：US20220318505A1

公开(公告)日：2022-10-06

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 ,  G06F40/126 ,  G06N3/04 ,  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：US20220138185A1

公开(公告)日：2022-05-05

申请号：US17087943

申请日：2020-11-03

Applicant: Adobe Inc.

Inventor： Quan Tran ,  Zhe Lin ,  Xuanli He ,  Walter Chang ,  Trung Bui ,  Franck Dernoncourt

IPC: G06F16/242 ,  G06F40/56 ,  G06N3/08 ,  G06N3/04

Abstract: Systems and methods for natural language processing are described. Embodiments are configured to receive a structured representation of a search query, wherein the structured representation comprises a plurality of nodes and at least one edge connecting two of the nodes, receive a modification expression for the search query, wherein the modification expression comprises a natural language expression, generate a modified structured representation based on the structured representation and the modification expression using a neural network configured to combine structured representation features and natural language expression features, and perform a search based on the modified structured representation.

公开(公告)号：US20210271707A1

公开(公告)日：2021-09-02

申请号：US16803480

申请日：2020-02-27

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Xihui Liu ,  Quan Tran ,  Jianming Zhang ,  Handong Zhao

IPC: G06F16/583 ,  G06K9/62 ,  G06K9/72 ,  G06F40/30 ,  G06F16/538 ,  G06F16/56 ,  G06F16/2457 ,  G06N3/08

Abstract: Certain embodiments involve a method for generating a search result. The method includes processing devices performing operations including receiving a query having a text input by a joint embedding model trained to generate an image result. Training the joint embedding model includes accessing a set of images and textual information. Training further includes encoding the images into image feature vectors based on spatial features. Further, training includes encoding the textual information into textual feature vectors based on semantic information. Training further includes generating a set of image-text pairs based on matches between image feature vectors and textual feature vectors. Further, training includes generating a visual grounding dataset based on spatial information. Training further includes generating a set of visual-semantic joint embeddings by grounding the image-text pairs with the visual grounding dataset. Additionally, operations include generating an image result for display by the joint embedding model based on the text input.

公开(公告)号：US20200090651A1

公开(公告)日：2020-03-19

申请号：US16133190

申请日：2018-09-17

Applicant: Adobe Inc.

Inventor： Quan Tran ,  Trung Bui ,  Hung Bui

IPC: G10L15/22 ,  G10L15/16 ,  G10L15/30 ,  G06N3/04 ,  G06N3/10

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.