Abstract: In implementations of systems for stylizing digital content, a computing device receives digital content having a plurality of content entities. Classified content entities are generated by classifying the plurality of content entities using one or more machine-learning models. A determination is then made regarding correspondence of the classified content entities with visual styles of a digital template. Based on the determined correspondence, the plurality of content entities of the digital content are displayed as having the visual styles, respectively, of the digital template.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: Systems and methods for natural language processing are described. Embodiments of the present disclosure receive plain text comprising a sequence of text entities; generate a sequence of entity embeddings based on the plain text, wherein each entity embedding in the sequence of entity embeddings is generated based on a text entity in the sequence of text entities; generate style information for the text entity based on the sequence of entity embeddings; and generate a document based on the style information.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: Systems and methods for natural language processing are described. Embodiments of the present disclosure receive plain text comprising a sequence of text entities; generate a sequence of entity embeddings based on the plain text, wherein each entity embedding in the sequence of entity embeddings is generated based on a text entity in the sequence of text entities; generate style information for the text entity based on the sequence of entity embeddings; and generate a document based on the style information.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for using machine learning techniques to generate section identifiers for one or more sections of the unstructured or unformatted text data. A document-processing application identifies, with a feature-prediction layer of a machine-learning model, a feature representation that represents a semantic structure of a text section within the unformatted and unstructured document. The document-processing application generates, with a sequence-prediction layer of the machine-learning model, a section identifier (e.g., heading, body, list) for a corresponding text section by applying the sequence-prediction layer to the feature representation and using contextual information of neighboring text sections.