Methods, systems and computer program products for multi-style text transformation are provided herein. A computer-implemented method includes selecting at least one set of style specifications for transforming at least a portion of input text. The at least one set of style specifications include one or more target writing style domains selected from a plurality of writing style domains, weights for each of the target writing style domains representing relative impact of the target writing style domains for transformation of at least a portion of the input text, and weights for each of a set of linguistic aspects for transformation of at least a portion of the input text. The computer-implemented method also includes generating one or more style-transformed output texts based at least in part on the at least one set of style specifications utilizing at least one unsupervised neural network.

Documents in source natural languages are translated into target natural languages using a computer-implemented translation that is configured to operate within the domain of the subject matter of the documents that imposes specialized requirements for translation and readability. Subject matter specific documents typically include domain-specific terminology, are subject to various regulatory guidelines, and have different readability requirements depending on the intended reader. The computer-implemented translation applies machine-learning techniques that deconstruct elements of the subject matter specific document into a standard data structure and perform pre-processing steps to tokenize digitized document text to identify the correct sentence structure and syntax for the target natural language to optimize translation by, e.g., a neural machine translation engine. The text segments that are input into the neural machine translation engine are generated to be semantically meaningful in the target natural language to thereby enhance the understanding of the neural machine translation engine.

A system and method for implementing slot-relation extraction for a task-oriented dialogue system that includes implementing dialogue intent classification machine learning models that predict a category of dialogue of a single utterance based on an input of utterance data relating to the single utterance, wherein the category of dialogue informs a selection of slot-filling machine learning models; implementing the slot-filling machine learning models that predict slot classification labels for each of a plurality of slots within the utterance based on the input of the utterance data; implementing a slot relation extraction machine learning model that predicts semantic relationship classifications between two or more distinct slots of tokens of the utterance; and generating a response to the single utterance or performing actions in response to the single utterance based on the semantic relationship classifications between the distinct pairings of the two or more distinct slots of the single utterance.

The present disclosure provides, among other things, methods and systems of managing communications, the method including: receiving a first communication; obtaining a user profile; comparing a user property from the user profile with a first content and a second content in the first communication; determining a first relevancy of the first content and a second relevancy of the second content; configuring a first content layout of the first communication based on the first relevancy and the second relevancy; and displaying the first content layout.

Systems and methods of editing a user experience by a what you see is what you get (wysiwyg) editor are disclosed. A system obtains a visible portion of a document object model for a document, with the visible portion including one or more visible objects to be presented to a user during a user experience. Instead of the visible portion being generated by the wysiwyg editor (thus requiring the visible portion to be rendered again at a later time by a rendering engine), the visible portion to be used is generated by the rendering engine based on a schema of the document. To allow such, the system generates and outputs an overlay to be placed on the visible portion during display of the visible portion, with the overlay to receive user inputs to the visible portion and thus prevent user interaction with the visible portion.

Methods and apparatuses are described for building a knowledge base taxonomy from structured or unstructured computer text for use in automated user interactions. A server computing device receives one or more of structured text or unstructured text corresponding to historical user interaction data from a database. The server computing device extracts one or more terms from the received text that are most relevant to a subject matter domain. The server computing device organizes the extracted one or more terms into a taxonomy data structure.

In some implementations, a method includes acquiring data about a plurality of web pages rendered within one or more browser tabs of a web browser executing on at least one computing device and processing the acquired data to group the plurality of web pages into one or more groups of web pages. The processing includes performing a similarity analysis using the acquired data, where each group includes web pages that are determined to be topically related to each other based on the similarity analysis. The method includes selecting a group of web pages from the one or more groups of web pages that are determined to be topically-related to content displayed in the web browser and providing a navigation suggestion for display on a user interface of the web browser based on the selected group.

A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

A system and method for detecting webpage zones of interest. A method includes receiving at least one webpage analysis request, wherein the received at least one webpage analysis request includes at least one webpage in a website; identifying, in the at least one webpage, at least one zone, wherein the at least one zone is a content element of a webpage; classifying the at least one zone into a category of interest, wherein the classification is based on a trained machine learning model configured to classify DOM elements of the least one webpage, and wherein a category of interest is a category determined based on a functionality of the website; and storing the classification by indicating the category of interest for each zone.

A system for monitoring and improving social agent interaction quality includes a computing platform having processing hardware and a system memory storing a software code. The processing hardware is configured to execute the software code to receive, from a social agent, interaction data describing an interaction of the social agent with a user, and to perform an assessment of the interaction, using the interaction data, as one of successful or including a flaw. When the assessment indicates that the interaction includes the flaw, the processing hardware is further configured to execute the software code to identify an interaction strategy for correcting the flaw, and to deliver, to the social agent, one or both of the assessment and the interaction strategy to correct the flaw in the interaction.