Abstract: A computerized method, system and computer program product for building a dialogue flow. One embodiment of the method may comprise receiving an input document, the input document comprising content, and generating, by a question-answer pipeline, a plurality of question-answer pairs from the content of the input document. For each question-answer pair, the method may further comprise feeding the question of the question-answer pair into an intent of a dialogue flow structure, and feeding the answer of the question-answer pair as one response of the intent. The method may further comprise tagging each of the plurality of question-answer pairs with a corresponding document section index, reading, by a conversational agent, the input document to a user, pausing the reading when the conversational agent reaches one of the document section indices in the input document, and in response, reading the question corresponding to the document section indicia to the user.

Abstract: Methods and apparatus for complementary evidence identification in natural language inference. A given question is obtained and a set of N passages is obtained from a database. A probability is determined, for each passage of the set of N passages, of a corresponding passage being a supportive passage for the given question and the set of N passages is ranked based on the determined probabilities. M passages that are ranked 1 to M of the set of N passages are selected. A set of L passages is selected based on a plurality of scores, each score assigned to a set of candidate passages of the set of N passages, each score being based on the determined probabilities, the selected M passages, and a weighted regulation parameter. The set of L passages is provided to a computerized machine learning system to answer the question based on the set of L passages.

Abstract: To improve actual labels that are produced by a black box computer classifier system from inputs, identify, using an environment-aware predictor and an environment-agnostic predictor, a subset of the inputs. The subset of the inputs has a stable correlation with the actual labels across a plurality of environments. Identify the subset of the inputs as an explanatory rationale for the actual labels. Display the explanatory rationale with the actual labels to a consumer of the actual labels. Optionally, in response to the explanatory rationale failing a rubric established by the consumer, generate revised inputs by removing the explanatory rationale from the inputs; and produce revised labels by processing the revised inputs with the environment-agnostic predictor.

Abstract: A method for identifying malicious software includes receiving and executing a software application, identifying a plurality of uniform resource identifiers the software application interacts with during execution of the software application, and generating a vector representation for the software application using a feed-forward neural network configured to receive the plurality of uniform resource identifiers as feature inputs. The method also includes determining similarity scores for a pool of training applications, each similarity score associated with a corresponding training application and indicating a level of similarity between the vector representation for the software application and a respective vector representation for the corresponding training application.

Abstract: A method for identifying malicious software includes receiving and executing a software application, identifying a plurality of uniform resource identifiers the software application interacts with during execution of the software application, and generating a vector representation for the software application using a feed-forward neural network configured to receive the plurality of uniform resource identifiers as feature inputs. The method also includes determining similarity scores for a pool of training applications, each similarity score associated with a corresponding training application and indicating a level of similarity between the vector representation for the software application and a respective vector representation for the corresponding training application.

Abstract: An artificial intelligence (AI) interaction method, system, and computer program product include selecting an artificial intelligence model to respond to a query to generating a response to the query using the selected artificial intelligence model, and receiving the response to the query from the selected artificial intelligence model.

Abstract: A method for identifying malicious software includes receiving and executing a software application, identifying a plurality of uniform resource identifiers the software application interacts with during execution of the software application, and generating a vector representation for the software application using a feed-forward neural network configured to receive the plurality of uniform resource identifiers as feature inputs. The method also includes determining similarity scores for a pool of training applications, each similarity score associated with a corresponding training application and indicating a level of similarity between the vector representation for the software application and a respective vector representation for the corresponding training application.

Abstract: A method (600) for identifying malicious software includes receiving and executing a software application (210), identifying a plurality of uniform resource identifiers (220) the software application interacts with during execution of the software application, and generating a vector representation (260) for the software application using a feed-forward neural network (170) configured to receive the plurality of uniform resource identifiers as feature inputs. The method also includes determining similarity scores (262) for a pool of training applications, each similarity score associated with a corresponding training application and indicating a level of similarity between the vector representation for the software application and a respective vector representation for the corresponding training application.

Abstract: A current observation expressed in natural language is received. Entities in the current observation are extracted. A relevant historical observation is retrieved, which has at least one of the entities in common with the current observation. The current observation and the relevant historical observation are combined as observations. The observations and a template list specifying a list of verb phrases to be filled-in with at least some of the entities are input to a neural network, which can output the template list of the verb phrases filled-in with said at least some of the entities. The neural network can include attention mechanism. A reward associated with the neural network's output can be received and fed back to the neural network for retraining the neural network.

Abstract: A method comprises receiving a new data set; identifying at least one prior data set of a plurality of prior data sets that matches the new data set; generating a natural language data science problem statement for the new data set based on information associated with the at least prior one data set that matches the new data set; outputting the generated natural language data science problem statement for user verification; and in response to receiving user input verifying the natural language generated data science problem statement, generating one or more AutoAI configuration settings for the new data set based on one or more AutoAI configuration settings associated with the at least one prior data set that matches the new data set.

Abstract: Systems, computer-implemented methods, and/or computer program products facilitating a process to identify and respond to a primary electronic message are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a determination component can determine that a primary electronic message has not received a response electronic message. An analysis component can generate a generated electronic message addressing the informational or emotional content of the primary electronic message. In one or more embodiments, an updating component can update the analytical model based on one or more feedbacks to the generated electronic message, where the analytical model can remain active while being updated.

Abstract: A method and system of determining an output label rationale are provided. A first generator receives a first class of data and selects one or more input features from the first class of data. A first predictor receives the one or more selected input features from the first generator and predicts a first output label. A second generator receives a second class of data and selects one or more input features from the second class of data. A second predictor receives the one or more selected input features from the second generator and predicts a second output label. A discriminator receives the first and second output labels and determines whether the selected one or more input features from the first class of data or the selected features of the one or more input features from the second class of data, more accurately represents the first output label.

Abstract: A method and a system for learning and applying neuro-symbolic multi-hop rules are provided. The method includes inputting training texts into a neural network as well as pre-defined entities. The training texts and the entities relate to a specific domain. The method also includes generating an entity graph made up of nodes and edges. The nodes represent the pre-defined entities, and the edges represent passages in the training texts with co-occurrence of the entities connected together by the edges. The method further includes determining a relation based on the passages for each of the pre-defined entities connected together by the edges, calculating a probability relating to the relation, generating a potential reasoning path between a head entity and a target entity. The method also includes learning a neuro-symbolic rule by converting the edges along the potential reasoning path into symbolic rules and combining those rules into the neuro-symbolic rule.

Abstract: A computer-implemented method includes using an embedding network to generate prototypical vectors. Each prototypical vector is based on a corresponding label associated with a first domain. The computer-implemented method also includes using the embedding network to generate an in-domain test vector based on at least one data sample from a particular label associated with the first domain and using the embedding network to generate an out-of-domain test vector based on at least one other data sample associated with a different domain. The computer-implemented method also includes comparing the prototypical vectors to the in-domain test vector to generate in-domain comparison values and comparing the prototypical vectors to the out-of-domain test vector to generate out-of-domain comparison values. The computer-implemented method also includes modifying, based on the in-domain comparison values and the out-of-domain comparison values, one or more parameters of the embedding network.

Abstract: A computerized method, system and computer program product for automatically generating question and answer pairs. One embodiment of the method may comprise receiving an input document, the input document comprising content. The method may further comprise generating, by a first machine learning model from the input document, a plurality of answers based on the content of the input document, and generating, by a second machine learning model from the input document, a question for each of the plurality of answers to form a plurality of question-answer pairs. The method may further comprise ranking, by a third machine learning model, the plurality of question-answer pairs, selecting a predetermined number of highest ranked question-answer pairs, and returning the predetermined number of highest ranked question-answer pairs to a user.

Abstract: A computerized method, system and computer program product for building a dialogue flow. One embodiment of the method may comprise receiving an input document, the input document comprising content, and generating, by a question-answer pipeline, a plurality of question-answer pairs from the content of the input document. For each question-answer pair, the method may further comprise feeding the question of the question-answer pair into an intent of a dialogue flow structure, and feeding the answer of the question-answer pair as one response of the intent. The method may further comprise tagging each of the plurality of question-answer pairs with a corresponding document section index, reading, by a conversational agent, the input document to a user, pausing the reading when the conversational agent reaches one of the document section indices in the input document, and in response, reading the question corresponding to the document section indicia to the user.

Abstract: Embodiments relate to a system, program product, and method for leveraging cognitive systems to facilitate the automated data table discovery for automated machine learning, and, more specifically, to leveraging a trained cognitive system to automatically search for additional data in an external data source that may be merged with an initial user-selected data table to generate a more robust machine learning model. Manual efforts to find and validate data appropriate for building and training a particular model for a particular task are significantly reduced. Specifically, a learning-based approach to leverage with machine learning models to automatically discover related datasets and join the datasets for a given initial dataset is disclosed herein. Operations that include dataset selection facilitate continued reinforcement learning of the systems.

Abstract: A method and system of training a natural language processing network are provided. A corpus of data is received and one or more input features selected therefrom by a generator network. The one or more selected input features from the generator network are received by a first predictor network and used to predict a first output label. A complement of the selected input features from the generator network are received by a second predictor network and used to predict a second output label.

Abstract: Systems, computer-implemented methods, and/or computer program products facilitating a process to identify and respond to a primary electronic message are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a determination component can determine that a primary electronic message has not received a response electronic message. An analysis component can generate a generated electronic message addressing the informational or emotional content of the primary electronic message. In one or more embodiments, an updating component can update the analytical model based on one or more feedbacks to the generated electronic message, where the analytical model can remain active while being updated.

Abstract: In an approach to generating question answer pairs, one or more computer processors receive a corpus of text. One or more computer processors extract one or more key concepts from the corpus of text. Based on the one or more key concepts, one or more computer processors generate one or more questions associated with the key concepts, where the one or more key concepts are answers to the one or more generated questions. One or more computer processors display the one or more generated questions and the answers to the one or more generated questions.