Abstract: A method and system for one or more application command recommendations may include receiving a search query, the search query being an in-application assistance query, accessing contextual data associated with the search query, providing at least one of the search query and the contextual data as input to a multilingual machine-learning (ML) model to identify one or more application command recommendations, obtaining the one or more application command recommendations as an output from the multilingual ML model, and providing data about the output to the application for display. The multilingual ML model uses a multilingual encoder to provide command recommendations for a plurality of languages.

Abstract: Techniques performed by a data processing system for analyzing the lexical difficulty of words of textual content include analyzing plurality of textual content sources to determine a first frequency at which each of a plurality of first words appears, analyzing search data to determine a second frequency at which each of the plurality of first words appear in searches for a definition, generating a lexical difficulty model based on the first frequency and the second frequency, the model is configured to receive a word as an input and to output a prediction for how difficult the word is likely to be for a user, receiving a request to analyze first textual content from a client device, analyzing the first textual content using the lexical difficulty model to generate lexical difficulty information, and sending a response to the client device that includes requested information.

Abstract: Recognizing problems in productivity flow for productivity applications can be accomplished by receiving a user profile and actions for a timeframe, where each action comprises an activity and timing; and identifying a grammar that captures action semantics and sequences in which they are executed that is similar to at least the actions for the timeframe, where the grammar corresponds to a help case. When the grammar is identified, corresponding help content for the help case can be retrieved and provided to the user.

Abstract: The discussion relates to unconscious bias detection. One example can detect potential bias words in a text sample and suggest alternative phrasing to eliminate the potential bias words from the text sample.

Abstract: Techniques are presented for providing misinformation detection in online content. The described techniques can identify instances of misinformation in online content and pass a misinformation result to the user. A misinformation probability analysis can be performed by applying a syntactic analysis and a semantic analysis to detect misinformation with confidence by applying featurization to a URL, text of content referenced by the URL, and metadata associated with the URL using a feature set, the feature set comprising semantic-based features and syntactic-based features, wherein the semantic features and the syntactic features are selected from the group consisting of: sentiment amplifiers, sentiment continuity disruption features, lexical features, keywords, baseline features, speech act, sensicon features, emotion detection on the obtained text, exaggerated language, strong adjectives, heuristics, bag-of-words, objectivity, colloquial-ness score, and semantic difference.

Abstract: The discussion relates to unconscious bias detection. One example can detect potential bias words in a text sample and suggest alternative phrasing to eliminate the potential bias words from the text sample.