Abstract: A method for log data sampling is disclosed. The method includes receiving logs of a computer system. A log comprises information regarding an operation of the computer system. The method also includes determining a sample of the logs by applying a set of sampling methods to the logs. The method further includes providing the sample of the logs as an input to an anomaly detection model for the computer system. The anomaly detection model identifies a fault in the operation of the computer system based on the input.

Abstract: Mechanisms are provided to implement a brand personality inference engine. The mechanisms receive crowdsource information and extract features associated with a brand from the crowdsource information. The crowdsource information comprises natural language content submitted by a plurality of providers to a crowdsource information source. The mechanisms analyze features associated with the brand in accordance with a brand personality model configured to predict a brand personality for the brand based on the features associated with the brand. The mechanisms generate an inferred brand personality data structure, representing a perceived brand personality of providers providing the crowdsource information, and output an output indicating aspects of the perceived brand personality based on the inferred brand personality data structure.

Abstract: A method for log data sampling is disclosed. The method includes receiving logs of a computer system. A log comprises information regarding an operation of the computer system. The method also includes determining a sample of the logs by applying a set of sampling methods to the logs. The method further includes providing the sample of the logs as an input to an anomaly detection model for the computer system. The anomaly detection model identifies a fault in the operation of the computer system based on the input.

Abstract: Aspects of the disclosure provide for a method. In at least some examples, the method includes receiving logs of a computer system, wherein a log comprises information regarding an operation of the computer system. The method also includes performing a course analysis of the logs to identify invariants, parameters, and gray area terms included in the logs, wherein gray area terms are terms included in the logs that are undefined between identifications of invariant or parameter by the course analysis. The method also includes performing a fine analysis to identify each of the gray area terms as either an invariant or a parameter to generate first log parsing templates, wherein the fine analysis is a sequence labeling analysis. The method also includes performing a similarity analysis to combine similar templates from among the first log parsing templates to form second log templates.

Abstract: A method, system and a computer program product are provided for generating a natural language model that is substantially independent of languages and domains by transforming monolingual embeddings into a multilingual embeddings in a first shared embedding space using a cross-lingual learning process, and then transforming the multilingual embeddings into cross-domain, multilingual embeddings in a second shared embedding space using a cross-domain learning process, where the multilingual embeddings and/or cross-domain, multilingual embeddings are evaluated to measure a degree to which the embeddings associate a set of target concepts with a set of attribute words.

Abstract: A system configured to proactively test a Named Entity Recognition model using crowdsourcing, the system comprising memory for storing instructions, and a processor configured to execute the instructions to receive the NER model as input; provide an explanation for predictions made by the NER model to a crowd; receive a test sample from a first crowd worker, the test sample is intended to generate an error for the NER model; generate a prediction using the NER model based on the test sample as input; receiving validation data corresponding to the prediction of the NER model; receive categorization data of the error corresponding to the test sample; and improve the NER model based on the test sample.

Abstract: A system configured to proactively test a log classification model using crowdsourcing, the system comprising memory for storing instructions, and a processor configured to execute the instructions to receive the log classification model as input; provide an explanation for predictions made by the log classification model to a crowd; receive a test sample from a first crowd worker, the test sample is intended to generate an error for the log classification model; generate a prediction using the log classification model based on the test sample as input; receiving validation data corresponding to the prediction of the log classification model; receive categorization data of the error corresponding to the test sample; and improve the log classification model based on the test sample.

Abstract: A system, computer program product, and method are disclosed. In an approach to train customer service agent using chatbots. The method includes training a chatbot for a customer chat simulation based on a customer service conversation data, a task scenario, and a customer persona. The method also includes monitoring an interaction between a customer service agent and the chatbot. The method further includes determining an assessment of the performance of the customer service agent based on the interaction between the customer service agent and the chatbot. The method additionally includes generating feedback for the customer service agent based on the assessment of the performance of the customer service agent.

Abstract: Embodiments for generating entity recognition models are provided. A data set including a plurality of entity references and a plurality of entity types are received. The plurality of entity types are divided into a plurality of entity type groups. The data set is divided into a plurality of data subsets. Each of the plurality of data subsets is associated with a respective one of the plurality of entity type groups. A plurality of entity recognition models are trained. Each of the plurality of entity recognition models is trained based on a respective one of the plurality of entity type groups and a respective one of the plurality of data subsets. A combined entity recognition model is generated based on the plurality of entity recognition models.

Abstract: A system, computer program product, and method are disclosed. In an approach to train customer service agent using chatbots. The method includes training a chatbot for a customer chat simulation based on a customer service conversation data, a task scenario, and a customer persona. The method also includes monitoring an interaction between a customer service agent and the chatbot. The method further includes determining an assessment of the performance of the customer service agent based on the interaction between the customer service agent and the chatbot. The method additionally includes generating feedback for the customer service agent based on the assessment of the performance of the customer service agent.

Abstract: A method, system, and a computer program product automatically select training data for updating a model by applying human-annotated training data to a model to generate results that are evaluated to identify correct case results and false case results that are categorized into error type categories for use in building error models corresponding to the error type categories, where each error model is built from at least failed case results belonging to a corresponding error type, and where unlabeled data samples are applied to each error model to compute an error likelihood for each unlabeled data sample with respect to each error type category, thereby enabling the selection and display of unlabeled data samples for annotation by a subject matter expert based on a computed error likelihood for the one or more unlabeled data samples in a specified error type category meeting or exceeding an error threshold requirement.

Abstract: A system configured to predict fine-grained affective states. The system comprising a processor configured to execute instructions to create training data comprising content conveying emotions, and to create a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The system uses the trained model to predict fine-grained affective states for text conveying an emotion.

Abstract: A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a conversation between a first participant and a second participant; receiving, utilizing the processor, an objective of the first participant for the conversation between the first participant and the second participant, where the objective is separate from the conversation; determining, utilizing the processor, a dialog act to be entered by the first participant that meets the objective, utilizing a model, including scoring a plurality of proposed dialog acts based on an amount that each proposed dialog act will change a probability of the objective being achieved during the conversation, and determining the dialog act to be entered, based on the scoring; and returning, utilizing the processor, the dialog act to the first participant.

Abstract: A computer-implemented method according to one embodiment includes receiving, utilizing a processor, textual data associated with a real-time conversation between a first participant and a second participant; receiving, utilizing the processor, an objective of the first participant for the real-time conversation between the first participant and the second participant; determining, utilizing the processor, a dialog act to be entered by the first participant at a current point in the real-time conversation that meets the objective, utilizing a model, including scoring a plurality of proposed dialog acts based on an amount that each proposed dialog act will change a probability of the objective being achieved during a current point in the real-time conversation, and determining the dialog act to be entered, based on the scoring; and returning, utilizing the processor, the dialog act to the first participant.

Abstract: A computer-implemented method for fine-grained affective states prediction. The computer-implemented method creates training data comprising content conveying emotions. The method creates a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The trained model can be used to predict fine-grained affective states for text conveying an emotion.

Abstract: A method, system and a computer program product are provided for aligning embeddings of multiple languages and domains into a shared embedding space by transforming monolingual embeddings into a multilingual embeddings in a first shared embedding space using a cross-lingual learning process, and then transforming the multilingual embeddings into cross-domain, multilingual embeddings in a second shared embedding space using a cross-domain learning process.

Abstract: A method, system and a computer program product are provided for generating a natural language model that is substantially independent of languages and domains by transforming monolingual embeddings into a multilingual embeddings in a first shared embedding space using a cross-lingual learning process, and then transforming the multilingual embeddings into cross-domain, multilingual embeddings in a second shared embedding space using a cross-domain learning process, where the multilingual embeddings and/or cross-domain, multilingual embeddings are evaluated to measure a degree to which the embeddings associate a set of target concepts with a set of attribute words.

Abstract: Methods, systems, and computer program products for a semi-automatic process for creating a natural language processing resource are provided herein. A computer-implemented method includes identifying multiple annotation tasks in connection with natural language processing of input text, and automatically determining, based on analysis of (i) parameters related to the identified annotation tasks and (ii) parameters related to annotation task users, routing instructions for the identified annotation tasks, wherein the routing instructions comprise (a) instructions to route a first sub-set of the identified annotation tasks to non-expert annotation task users and (b) instructions to route a second sub-set of the identified annotation tasks to expert annotation task users.

Abstract: A computer-implemented method for fine-grained affective states prediction. The computer-implemented method creates training data comprising content conveying emotions. The method creates a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The trained model can be used to predict fine-grained affective states for text conveying an emotion.

Abstract: A system configured to predict fine-grained affective states. The system comprising a processor configured to execute instructions to create training data comprising content conveying emotions, and to create a trained model by performing an emotion vector space model training process using the training data to train a model using a feed forward neural network that converts discrete emotions into emotion vector representations. The system uses the trained model to predict fine-grained affective states for text conveying an emotion.