Adobe, Inc.

Aparna Garimella

Niyati Himanshu Chhaya

Balaji Vasan Srinivasan

Sumit Shekhar

Natwar Modani

Vinay Aggarwal

Sharmila Reddy Nangi

Vishwa Vinay

Fan Du

Vlad Ion Morariu

Nandakishore Kambhatla

Srikrishna Karanam

Ashutosh Mehra

Anandha Velu Natarajan

Saurabh Mahapatra

Sagnik Mukherjee

Aniruddha Mahapatra

Sapthotharan Krishnan Nair

Bhanu Prakash Reddy Guda

Manoj Kilaru

Navita Goyal

Suryateja Bv

Inderjeet Nair

Garvit Bhardwaj

Akhash N A

Vishesh Kaushik

Muskan Agarwal

Priyanshu Agarwal

Elaine Chao

Snehal Raj

Jay Mundra

Atharv Tyagi

Ananya Ganesh

Embodiments are disclosed for a digital design system trained to segment unstructured text into topically coherent segments. The method may include receiving unstructured text, the unstructured text including a sequence of sentences. The disclosed systems and methods further comprise generating, by a neural network, a hierarchically segmented tree structure representing the unstructured text. The tree structure comprises a plurality of tree structure nodes, where a node of the tree structure nodes represents a sentence from the sequence of sentences. The segments and sub-segments of the unstructured text can then be determined based on node data for nodes of the hierarchically segmented tree structure. Using the determined segments and sub-segments of the unstructured text, a modified representation of the unstructured text can be displayed.

Hierarchical segmentation of unstructured text using neural networks

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

Systems and methods for text simplification are described. Embodiments of the present disclosure identify a simplified text that includes original information from a complex text and additional information that is not in the complex text. Embodiments then compute an entailment score for each sentence of the simplified text using a neural network, wherein the entailment score indicates whether the sentence of the simplified text includes information from a sentence of the complex text corresponding to the sentence of the simplified text. Then, embodiments generate a modified text based on the entailment score, the simplified text, and the complex text, wherein the modified text includes the original information and excludes the additional information. Embodiments may then present the modified text to a user via a user interface.

Text simplification with minimal hallucination

A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.

Auto-creation of custom models for text summarization

Embodiments are disclosed for generating an intelligent change summary are described. In some embodiments, a method of generating an intelligent change summary includes obtaining a representation of a plurality of versions of a document, determining a distance score based on a comparison of a first of version of the document and a second version of the document, the distance score representing a magnitude of changes made from the first version of the document to the second version of the document, and generating a change summary of the document based on the distance score.

Intelligent change summarization for designers

Embodiments provide systems, methods, and computer storage media for extracting semantic labels for field widgets of form fields in unfilled forms. In some embodiments, a processing device accesses a representation of a fillable widget of a form field of an unfilled form. The processing device generates an encoded input representing text and layout of a sequence of tokens in a neighborhood of the fillable widget. The processing device uses a machine learning model to extract a semantic label representing a field type of the fillable widget in view of the encoded input. The processing device causes execution of an action using the semantic label.

Automatic semantic labeling of form fields with limited annotations

Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

Facilitating generation of fillable document templates

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Telangana, India

Aparna Garimella