Adobe, Inc.

Amir Pouran Ben Veyseh

Franck Dernoncourt

Walter Chang

Ning Xu

Quan Anh Tran

Doo Soon Kim

Rajiv Jain

Varun Manjunatha

Lidan Wang

Systems and methods for natural language processing are described. Embodiments of the present disclosure receive an input phrase including an aspect term; generate a complement phrase based on the input phrase using a language generator model, wherein the complement phrase includes different words than the input phrase; combine a representation of the input phrase and a representation of the complement phrase to obtain an augmented representation of the input phrase; and generate sentiment information corresponding to the aspect term based on the augmented representation.

Generative model for aspect term extraction

Systems and methods for item recommendation are described. One or more embodiments of the systems and methods include generating a hidden vector representation for each word of a source document; removing at least one word from the source document based on the hidden vector representation using a summarization network to obtain a summary document; filtering a plurality of candidate documents based on the source document to obtain a plurality of filtered candidate documents; comparing the summary document to each of the filtered candidate documents to obtain a ranking score for each of the filtered candidate documents; and identifying a relevant candidate document from the filtered candidate documents based on the ranking score.

Tutorial recommendation using discourse-level consistency and ontology-based filtering

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure receive a span of text comprising an offensive span and a non-offensive span, generate a contextualized word embedding for each of a plurality of words of the span of text, generate a refined vector representation for each of the plurality of words based on the corresponding contextualized word embedding using a refinement network trained for offensive text recognition, generate label information for each of the plurality of words based on the corresponding refined vector representation, wherein the label information indicates whether each of the plurality of words includes offensive text, and transmit an indication of a location of the offensive span based on the label information.

Transfer learning and prediction consistency for detecting offensive spans of text

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure generate a word representation vector for each word of a text comprising an event trigger word and an argument candidate word; generate a dependency tree based on the text and the word representation vector; determine that at least one word of the text is independent of a relationship between the event trigger word and the argument candidate word; remove the at least one word from the dependency tree based on the determination to obtain a pruned dependency tree; generate a modified representation vector for each word of the pruned dependency tree using a graph convolutional network (GCN); and identify the relationship between the event trigger word and the argument candidate word based on the modified representation vector for each word of the pruned dependency tree.

Event understanding with deep learning

Systems and methods for similarity search are described. Embodiments identify a document and a query corresponding to a matching phrase in the document, encode the query and a candidate phrase, score the candidate phrase using at least one learning-based score and at least one surface form score, wherein the at least one learning based score is based on the encoding, and the at least one surface form score is based on a surface form of the query and a surface form of the candidate phrase, and select the matching phrase based on the scoring.

Semantic phrasal similarity

Systems and methods for natural language processing are described. Embodiments of the inventive concept are configured to receive an input sequence and a plurality of candidate long forms for a short form contained in the input sequence, encode the input sequence to produce an input sequence representation, encode each of the plurality of candidate long forms to produce a plurality of candidate long form representations, wherein each of the candidate long form representations is based on a plurality of sample expressions and each of the sample expressions includes a candidate long form and contextual information, compute a plurality of similarity scores based on the candidate long form representations and the input sequence representation, and select a long form for the short form based on the plurality of similarity scores.

Acronym definition network

The present disclosure provides systems and methods for relationship extraction. Embodiments of the present disclosure provide a relationship extraction network trained to identify relationships among entities in an input text. The relationship extraction network is used to generate a dependency path between entities in an input phrase. The dependency path includes a set of words that connect the entities, and is used to predict a relationship between the entities. In some cases, the dependency path is related to a syntax tree, but it may include additional words, and omit some words from a path extracted based on a syntax tree.

Dependency path reasoning for measurement extraction

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.

Inducing rich interaction structures between words for document-level event argument extraction

Systems and methods for natural language processing are described. One or more embodiments of the present disclosure generate a word embedding for each word of an input phrase, wherein the input phrase indicates a sentiment toward an aspect term, compute a gate vector based on the aspect term, identify a dependency tree representing relations between words of the input phrase, generate a representation vector based on the dependency tree and the word embedding using a graph convolution network, wherein the gate vector is applied to a layer of the graph convolution network, and generate a probability distribution over a plurality of sentiments based on the representation vector.

Aspect-based sentiment analysis

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.

Machine learning models for detecting topic divergent digital videos

Techniques are described herein for determining a long-form of an abbreviation using a machine learning based approach that takes into consideration both sequential context and structural context, where the long-form corresponds to a meaning of the abbreviation as used in a sequence of words that form a sentence. In some embodiments, word representations are generated for different words in the sequence of words, and a combined representation is generated for the abbreviation based on a word representation corresponding to the abbreviation, a sequential context representation, and a structural context representation. The sequential context representation can be generated based on word representations for words positioned near the abbreviation. The structural context representation can be generated based on word representations for words that are syntactically related to the abbreviation. The combined representation can be input to a classification neural network trained to output a label representing the long-form of the abbreviation.

Machine learning based abbreviation expansion

A system, method and non-transitory computer readable medium for editing images with verbal commands are described. Embodiments of the system, method and non-transitory computer readable medium may include an artificial neural network (ANN) comprising a word embedding component configured to convert text input into a set of word vectors, a feature encoder configured to create a combined feature vector for the text input based on the word vectors, a scoring layer configured to compute labeling scores based on the combined feature vectors, wherein the feature encoder, the scoring layer, or both are trained using multi-task learning with a loss function including a first loss value and an additional loss value based on mutual information, context-based prediction, or sentence-based prediction, and a command component configured to identify a set of image editing word labels based on the labeling scores.

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Eugene, OR, United States of America

Amir Pouran Ben Veyseh