Abstract: A method, a structure, and a computer system for multilingual learning. The exemplary embodiments may include training, for each language in a set of two or more languages, a model for a task and identifying one or more important words appearing in at least two of the models. The exemplary embodiments may further include weighting one or more conflicts and one or more overlaps between the one or more important words, as well as generating a cluster of at least two languages of the set based on an aggregate of the weighting.

Abstract: Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.

Abstract: Techniques for using machine learning to model climatic data are disclosed. In one example, a computer implemented method comprises receiving climate data comprising a plurality of spatial components and a plurality of temporal components, and masking a portion of the climate data. A machine learning model is trained, wherein the training is based at least in part on the masked portion of the climate data. A vector representation of the climate data is generated via the machine learning model.

Abstract: One embodiment provides a method, including: receiving, in an environment for developing a conversational tutor, domain content corresponding to a domain of the conversational tutor, wherein the conversational tutor comprises a conversational agent designed to assist students in learning a domain; receiving, at the environment, tutoring content developed by a user, wherein the tutoring content comprises a tutoring script comprising questions to be presented to the student during a tutoring session with the conversational tutor to assist the student in learning the domain; evaluating, within the environment, the tutoring content against the domain content, wherein the evaluating comprises (i) identifying missing tutoring content, (ii) identifying the difficulty of the tutoring content, and (iii) identifying question dependencies; and providing, at the environment, at least one recommendation, identified from results of the evaluation, for adjusting the tutoring content.

Abstract: One embodiment provides a method, including: receiving a plurality of transcripts, each transcript corresponding to an interaction between a student and a tutoring agent; identifying conversational patterns within the plurality of transcripts, the identified conversational patterns indicating a level of understanding regarding the interaction between the tutoring agent and the student, wherein the identifying comprises (i) identifying an exchange between the tutoring agent and the student, the exchange comprising a conversational turn by each of the tutoring agent and the student and (ii) classifying each of the conversational turns into a response type; determining, based upon the identified conversational patterns, a possible improvement to the tutoring agent, wherein the possible improvement is determined based upon the classified response types of each of (i) the tutoring agent and (ii) the student; and notifying a developer of the tutoring agent of the identified possible improvement.

Abstract: Methods, systems and computer program products for automated learning are provided herein. A computer-implemented method includes authenticating a plurality of users for an automated learning session, wherein the plurality of users correspond to at least one device, and providing the automated learning session for the plurality of users. Providing the automated learning session comprises analyzing a plurality of learning models corresponding to one or more of the plurality of users, determining, based on the analysis, one or more activities to be performed by the plurality of users during the automated learning session, and executing the one or more activities on at least one device.

Abstract: One embodiment provides a method, including: receiving, in an environment for developing a conversational tutor, domain content corresponding to a domain of the conversational tutor, wherein the conversational tutor comprises a conversational agent designed to assist students in learning a domain; receiving, at the environment, tutoring content developed by a user, wherein the tutoring content comprises a tutoring script comprising questions to be presented to the student during a tutoring session with the conversational tutor to assist the student in learning the domain; evaluating, within the environment, the tutoring content against the domain content, wherein the evaluating comprises (i) identifying missing tutoring content, (ii) identifying the difficulty of the tutoring content, and (iii) identifying question dependencies; and providing, at the environment, at least one recommendation, identified from results of the evaluation, for adjusting the tutoring content.

Abstract: One embodiment provides a method, including: receiving a plurality of transcripts, each transcript corresponding to an interaction between a student and a tutoring agent; identifying conversational patterns within the plurality of transcripts, the identified conversational patterns indicating a level of understanding regarding the interaction between the tutoring agent and the student, wherein the identifying comprises (i) identifying an exchange between the tutoring agent and the student, the exchange comprising a conversational turn by each of the tutoring agent and the student and (ii) classifying each of the conversational turns into a response type; determining, based upon the identified conversational patterns, a possible improvement to the tutoring agent, wherein the possible improvement is determined based upon the classified response types of each of (i) the tutoring agent and (ii) the student; and notifying a developer of the tutoring agent of the identified possible improvement.