Abstract: Systems and methods are directed to providing personalized text proofing. A user model that is used to personalize generic critiques for text proofing a document is generated based on user signals indicating past user actions. During runtime of an application used to create the document, the user model is accessed and locally cached. User inputs comprising typed components used to create the document are received and a set of one or more generic critiques for the user inputs is accessed from an editor system. The user model is applied to the set which may modify a generic critique of the set. The modifying of the generic critique can cause the generic critique to be automatically applied or suppressed at the client device. The set including the modified generic critique is transmitted to a user device, whereby the user device applies the set to the document including automatically applying or suppressing the modified generic critique.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: Systems and methods for generating artificial intelligence (AI) writing assistance include receiving a writing prompt to be used by an AI writing engine from a writing assistance client. The writing prompt is processed to provide indication of relevant user content items that can be referenced in the writing prompt. The content and metadata for any content items referenced in the writing prompts is retrieved and aggregated with the writing prompt to generate a request for writing assistance for the AI writing engine. Once writing feedback is received, the feedback is provided to the writing assistance wherein it can be displayed in the UI for acceptance by a user.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a writing assistant configured to provide suggestions for improving the textual content. The textual content is analyzed by one or more NLP models configured to receive the textual content as an input and to provide candidate suggestions for correcting errors and/or otherwise improving the textual content. The candidate suggestions are associated with a suggestion category, and a determination is made by an autocorrection model whether autocorrection is appropriate for the suggestion category. The textual content is autocorrected based on those candidate suggestions for which autocorrection is appropriate while the remainder of the candidate suggestions may be presented to the user for approval prior to applying the candidate suggestions to the textual content.

Abstract: A computer implemented method includes receiving text corresponding to a span of text. Language detection is performed on the received text to generate multiple language candidates. The received text is also proofed over multiple languages to generate multiple language proofing critiques. A most likely language is generated for the received text as a function of the language candidates and the multiple language proofing critiques.

Abstract: A data processing system implements receiving textual content from a first application on a first client device associated with a first user for analysis by a context adaptive writing assistant configured to provide suggestions for improving the textual content, obtaining contextual information indicative of a level of formality of the textual content; and categorizing the textual content as being associated with a first level of formality selected from a plurality of levels of formality. The system is further implements analyzing the textual content to identify one or more suggested improvements to the textual content; selecting a subset of suggested improvements from the one or more suggested improvements to the textual content to the first user based on the first level of formality associated with the textual content; sending the subset of suggested improvements to the first client device; and causing the first client device to display the subset of suggested improvements.

Abstract: An editor service receives a textual input. The editor service provides the textual input to a rule-based grammar checker to obtain a grammar checker result. The editor service also provides the textual input to a machine learning (ML) fluency model that checks the textual input for errors and provides a ML model result. The editor service aggregates the grammar checker result and the ML model result and generates an editor service output based upon the aggregated results. A representation of the editor service result is provided to the client computing system for surfacing through a user interface.

Abstract: Systems and methods are directed to providing personalized text proofing. A user model that is used to personalize generic critiques for text proofing a document is generated based on user signals indicating past user actions. During runtime of an application used to create the document, the user model is accessed and locally cached. User inputs comprising typed components used to create the document are received and a set of one or more generic critiques for the user inputs is accessed from an editor system. The user model is applied to the set which may modify a generic critique of the set. The modifying of the generic critique can cause the generic critique to be automatically applied or suppressed at the client device. The set including the modified generic critique is transmitted to a user device, whereby the user device applies the set to the document including automatically applying or suppressing the modified generic critique.

Abstract: An editor service receives a textual input. The editor service provides the textual input to a rule-based grammar checker to obtain a grammar checker result. The editor service also provides the textual input to a machine learning (ML) fluency model that checks the textual input for errors and provides a ML model result. The editor service aggregates the grammar checker result and the ML model result and generates an editor service output based upon the aggregated results. A representation of the editor service result is provided to the client computing system for surfacing through a user interface.