Adobe, Inc.

Ganesh Karbhari Palwe

Balaji Krishnamurthy

Nikaash Puri

Milan Aggarwal

Mihir Naware

Pinkesh Badjatiya

Yaman Kumar

Hyman Chung

Matthew William Rozen

Simra Shahid

Jayakumar Subramanian

Pranjal Prasoon

Somesh Singh

Nikitha Srikanth

S Sejal Naidu

Sharat Chandra Racha

Nisarg Shailesh Joshi

Seoyoung Park

Nithyakala Sainath

Shashank Shailabh

Madhur Panwar

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.

System and methods for neural topic modeling using topic attention networks

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Uttar Pradesh, India

Ganesh Karbhari Palwe