Systems and methods may be used to generate and use intent predictions to enhance user experience. The intent predictions may describe the data required to resolve a user request included in a user input (e.g., question, search query, and the like) submitted by a user. The intent predictions may be generated using a machine learning model that comprises a model framework for extracting features and classifying user inputs into intent classes based on the extracted features. The intent predictions may be integrated into an information service to improve business metrics including contact rate, transfer rate, helpful rate, and net total promoter score.

An employment recruitment method based on face recognition includes acquiring a candidate's data from a third-party website, analyzing the candidate's data by a semantic analysis method to identify human resources information of the candidate, and analyzing messages and postings in the human resources information of the candidate to determine candidate's personality. A terminal device acquires a second face image of the candidate by a second camera, analyzes the second face image of the candidate by a computer vision algorithm to determine a micro-expression of the candidate, and provides the candidate's human resources information, the candidate's personality, and the candidate's micro-expression to the recruiter to evaluate the candidate. The terminal device applying the method is also disclosed.

An employment recruitment method based on face recognition includes acquiring a candidate's data from a third-party website, analyzing the candidate's data by a semantic analysis method to identify human resources information of the candidate, and analyzing messages and postings in the human resources information of the candidate to determine candidate's personality. A terminal device acquires a second face image of the candidate by a second camera, analyzes the second face image of the candidate by a computer vision algorithm to determine a micro-expression of the candidate, and provides the candidate's human resources information, the candidate's personality, and the candidate's micro-expression to the recruiter to evaluate the candidate. The terminal device applying the method is also disclosed.

A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.

A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.

The present disclosure provides, among other things, methods and systems of managing communications, the methods and systems including: receiving a first group communication from a first group; determining, based on the first group communication, a first group sentiment; receiving a first communication with a request to send the first communication to the first group; determining, based on the first communication, a first communication sentiment; comparing the first group sentiment with the first communication sentiment; and based on the comparing, performing an action on the request to send the first communication.

The present disclosure provides, among other things, methods and systems of managing communications, the methods and systems including: receiving a first group communication from a first group; determining, based on the first group communication, a first group sentiment; receiving a first communication with a request to send the first communication to the first group; determining, based on the first communication, a first communication sentiment; comparing the first group sentiment with the first communication sentiment; and based on the comparing, performing an action on the request to send the first communication.

This disclosure provides a system and method for providing intelligently-selected collections of user-centric content in a web browser. When implemented as a method, the method includes maintaining a user-centric graph with a plurality of user-centric facts derived from user interaction with different computer services. The method further includes recognizing different contexts of interest to the user. For each context, a collection of user-centric facts pertaining to the context are recognized in the user-centric graph, such recognition being based on a relationship between user-centric facts in the user-centric graph. The method further includes, for each context, displaying intelligently-selected content based on the collection of user-centric facts.

This disclosure provides a system and method for providing intelligently-selected collections of user-centric content in a web browser. When implemented as a method, the method includes maintaining a user-centric graph with a plurality of user-centric facts derived from user interaction with different computer services. The method further includes recognizing different contexts of interest to the user. For each context, a collection of user-centric facts pertaining to the context are recognized in the user-centric graph, such recognition being based on a relationship between user-centric facts in the user-centric graph. The method further includes, for each context, displaying intelligently-selected content based on the collection of user-centric facts.

A document analysis device that includes a memory operable to store a machine learning model configured to receive a sentence as an input and to output a classification identifier that is associated with a sentence type for the received sentence. The device further includes an artificial intelligence (AI) processing engine configured to receive a document comprising text, to sentences within the document, and to classify the sentences using the machine learning model. The AI processing engine is further configured to identify tagging rules for the document and to annotate one or more sentences from the document with a sentence type that matches a sentence type that is identified by the tagging rules for the document.