公开(公告)号：US11307881B1

公开(公告)日：2022-04-19

申请号：US17095290

申请日：2020-11-11

Applicant: Adobe Inc.

Inventor： Ripul Bhutani ,  Oliver Markus Michael Brdiczka ,  Doo Soon Kim ,  Aliakbar Darabi ,  Yinglan Ma

IPC: G06F3/0482 ,  G06F9/451 ,  G06N5/02 ,  G06F16/23

Abstract: In implementations of systems for generating suggestions with knowledge graph embedding vectors, a computing device implements a suggestion system to receive input data describing user interactions with an application for editing digital content. The suggestion system generates input embedding vectors based on the user interactions with the application and determines an item based on the input embedding vectors and knowledge graph embedding vectors generated from nodes of a knowledge graph describing a tutorial for editing digital content. The suggestion system generates an indication of the item for display in a user interface of a display device.

公开(公告)号：US20220114476A1

公开(公告)日：2022-04-14

申请号：US17070568

申请日：2020-10-14

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Tuan Manh Lai ,  Quan Tran ,  Doo Soon Kim

IPC: G06N20/00 ,  G06F16/248

Abstract: This disclosure describes one or more implementations of a text sequence labeling system that accurately and efficiently utilize a joint-learning self-distillation approach to improve text sequence labeling machine-learning models. For example, in various implementations, the text sequence labeling system trains a text sequence labeling machine-learning teacher model to generate text sequence labels. The text sequence labeling system then creates and trains a text sequence labeling machine-learning student model utilizing the training and the output of the teacher model. Upon the student model achieving improved results over the teacher model, the text sequence labeling system re-initializes the teacher model with the learned model parameters of the student model and repeats the above joint-learning self-distillation framework. The text sequence labeling system then utilizes a trained text sequence labeling model to generate text sequence labels from input documents.

公开(公告)号：US20200372025A1

公开(公告)日：2020-11-26

申请号：US16420764

申请日：2019-05-23

Applicant: ADOBE INC.

Inventor： Seung-hyun Yoon ,  Franck Dernoncourt ,  Trung Huu Bui ,  Doo Soon Kim ,  Carl Iwan Dockhorn ,  Yu Gong

IPC: G06F16/2452 ,  G06F16/2457 ,  G06F16/28 ,  G06F16/248 ,  G06N20/00

Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for techniques for identifying textual similarity and performing answer selection. A textual-similarity computing model can use a pre-trained language model to generate vector representations of a question and a candidate answer from a target corpus. The target corpus can be clustered into latent topics (or other latent groupings), and probabilities of a question or candidate answer being in each of the latent topics can be calculated and condensed (e.g., downsampled) to improve performance and focus on the most relevant topics. The condensed probabilities can be aggregated and combined with a downstream vector representation of the question (or answer) so the model can use focused topical and other categorical information as auxiliary information in a similarity computation. In training, transfer learning may be applied from a large-scale corpus, and the conventional list-wise approach can be replaced with point-wise learning.

公开(公告)号：US20190392066A1

公开(公告)日：2019-12-26

申请号：US16019152

申请日：2018-06-26

Applicant: Adobe Inc.

Inventor： Doo Soon Kim ,  Walter Chang

IPC: G06F17/30 ,  G06F17/27

Abstract: Various embodiments describe techniques for retrieving query results for natural language procedural queries. A query answering (QA) system generates a structured semantic representation of a natural language query. The structured semantic representation includes terms in the natural language query and the relationship between the terms. The QA system retrieves a set of candidate query results for the natural language query from a repository, generates a structured semantic representation for each candidate query result, and determines a match score between the natural language query and each respective candidate query result based on the similarity between the structured semantic representations for the natural language query and each respective candidate query result. A candidate query result having the highest match score is selected as the query result for the natural language query. In some embodiments, paraphrasing rules are generated from user interaction data and are used to determine the match score.

公开(公告)号：US20190278835A1

公开(公告)日：2019-09-12

申请号：US15915775

申请日：2018-03-08

Applicant: Adobe Inc.

Inventor： Arman Cohan ,  Walter W. Chang ,  Trung Huu Bui ,  Franck Dernoncourt ,  Doo Soon Kim

IPC: G06F17/22 ,  G06N3/04 ,  G06F17/30 ,  G06F17/27

Abstract: Techniques are disclosed for abstractive summarization process for summarizing documents, including long documents. A document is encoded using an encoder-decoder architecture with attentive decoding. In particular, an encoder for modeling documents generates both word-level and section-level representations of a document. A discourse-aware decoder then captures the information flow from all discourse sections of a document. In order to extend the robustness of the generated summarization, a neural attention mechanism considers both word-level as well as section-level representations of a document. The neural attention mechanism may utilize a set of weights that are applied to the word-level representations and section-level representations.

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：US20230403175A1

公开(公告)日：2023-12-14

申请号：US17806751

申请日：2022-06-14

Applicant: ADOBE INC.

Inventor： Tuan Manh Lai ,  Trung Huu Bui ,  Doo Soon Kim

IPC: H04L12/18 ,  G06F40/284 ,  G06N3/04

CPC classification number: H04L12/1831 ,  G06F40/284 ,  G06N3/04

Abstract: Systems and methods for coreference resolution are provided. One aspect of the systems and methods includes inserting a speaker tag into a transcript, wherein the speaker tag indicates that a name in the transcript corresponds to a speaker of a portion of the transcript; encoding a plurality of candidate spans from the transcript based at least in part on the speaker tag to obtain a plurality of span vectors; extracting a plurality of entity mentions from the transcript based on the plurality of span vectors, wherein each of the plurality of entity mentions corresponds to one of the plurality of candidate spans; and generating coreference information for the transcript based on the plurality of entity mentions, wherein the coreference information indicates that a pair of candidate spans of the plurality of candidate spans corresponds to a pair of entity mentions that refer to a same entity.

公开(公告)号：US20230267726A1

公开(公告)日：2023-08-24

申请号：US17651771

申请日：2022-02-18

Applicant: Adobe Inc.

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

IPC: G06V10/86 ,  G06V10/82 ,  G06V10/80 ,  G06V10/77 ,  G06N3/08 ,  G06N3/04 ,  G06F40/284

CPC classification number: G06V10/86 ,  G06V10/82 ,  G06V10/806 ,  G06V10/7715 ,  G06N3/088 ,  G06N3/0445 ,  G06F40/284

Abstract: Embodiments of the disclosure provide a machine learning model for generating a predicted executable command for an image. The learning model includes an interface configured to obtain an utterance indicating a request associated with the image, an utterance sub-model, a visual sub-model, an attention network, and a selection gate. The machine learning model generates a segment of the predicted executable command from weighted probabilities of each candidate token in a predetermined vocabulary determined based on the visual features, the concept features, current command features, and the utterance features extracted from the utterance or the image.

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：US11561969B2

公开(公告)日：2023-01-24

申请号：US16834850

申请日：2020-03-30

Applicant: Adobe Inc.

Inventor： Doo Soon Kim ,  Anthony M Colas ,  Franck Dernoncourt ,  Moumita Sinha ,  Trung Bui

IPC: G06F16/00 ,  G06F16/242 ,  G06F16/28

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.