Adobe, Inc.

Anandha Velu Natarajan

Balaji Vasan Srinivasan

Navita Goyal

Abhilasha Sancheti

Niyati Himanshu Chhaya

Vishwa Vinay

Natwar Modani

Sopan Khosla

Pranav Ravindra Maneriker

Cedric Huesler

Aparna Garimella

Sharmila Reddy Nangi

Aniruddha Mahapatra

Baldip Singh Bijlani

Ishika Singh

Priyanshu Gupta

Vipul Shankhpal

Embodiments of the present invention provide systems, methods, and computer storage media for pre-training entity extraction models to facilitate domain adaptation in resource-constrained domains. In an example embodiment, a first machine learning model is used to encode sentences of a source domain corpus and a target domain corpus into sentence embeddings. The sentence embeddings of the target domain corpus are combined into a target corpus embedding. Training sentences from the source domain corpus within a threshold of similarity to the target corpus embedding are selected. A second machine learning model is trained on the training sentences selected from the source domain corpus.

Pre-training techniques for entity extraction in low resource domains

In some embodiments, a style transfer computing system receives, from a computing device, an input text and a request to transfer the input text to a target style combination including a set of target styles. The system applies a style transfer language model associated with the target style combination to the input text to generate a transferred text in the target style combination. The style transfer language model comprises a cascaded language model configured to generate the transferred text. The cascaded language model is trained using a set of discriminator models corresponding to the set of target styles. The system provides, to the computing device, the transferred text.

Multi-dimensional language style transfer

This disclosure involves executing machine-learning techniques for transforming or otherwise processing electronic data. This disclosure, for example, relates to executing machine-learning techniques to generate data-verification indicators that augment electronic documents to represent the veracity of text. The machine-learning techniques include neural networks trained to retrieve and analyze evidence regarding content of electronic documents and to generate indicators of veracity to be displayed with that content via electronic reading software.

Machine-learning techniques for augmenting electronic documents with data-verification indicators

In some embodiments, a style transfer computing system generates a set of discriminator models corresponding to a set of styles based on a set of training datasets labeled for respective styles. The style transfer computing system further generates a style transfer language model for a target style combination that includes multiple target styles from the set of styles. The style transfer language model includes a cascaded language model and multiple discriminator models selected from the set of discriminator models. The style transfer computing system trains the style transfer language model to minimize a loss function containing a loss term for the cascaded language model and multiple loss terms for the multiple discriminator models. For a source sentence and a given target style combination, the style transfer computing system applies the style transfer language model on the source sentence to generate a target sentence in the given target style combination.

A fact replacement and style consistency tool is described. Rather than rely heavily on human involvement to replace facts and maintain consistent styles across multiple digital documents, the described change management system identifies factual and stylistic inconsistencies between these documents, in part, using natural language processing techniques. Once these inconsistencies are identified, the change management system generates a user interface that includes indications of the inconsistencies and information describing them, e.g., an indication noting not only a type of inconsistency but also presenting a first portion and at least a second portion of the multiple documents that are factually inconsistent. By automatically identifying these factual and stylistic inconsistencies across multiple documents and presenting indications of such cross-document inconsistencies, the described change management system eliminates human errors in connection with maintaining factual and stylistic consistency over a body of documents.

Fact replacement and style consistency tool

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Bengaluru, India

Anandha Velu Natarajan