Google Inc.

Spurthi Amba Hombaiah

Marc Alexander Najork

Mike Bendersky

Vladimir Ofitserov

Tao Chen

Michael Bendersky

Mingyang Zhang

Cheng Li

Te-Lin Wu

Systems and methods for document processing that can process and understand the layout, text size, text style, and multimedia of a document can generate more accurate and informed document representations. The layout of a document paired with text size and style can indicate what portions of a document are possibly more important, and the understanding of that importance can help with understanding of the document. Systems and methods utilizing a hierarchical framework that processes the block-level and the document-level of a document can capitalize on these indicators to generate a better document representation.

Layout-aware multimodal pretraining for multimodal document understanding

Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.

Training and/or utilizing a model for predicting measures reflecting both quality and popularity of content

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San Jose, CA, United States of America

Spurthi Amba Hombaiah