Abstract: One embodiment provides a method, including: monitoring a first chat channel within a distributed collaboration environment; identifying an outage affecting a service associated with the first chat channel; providing, to at least a second chat channel associated with at least a second service, a notification of the outage affecting the service, wherein the at least a second service is identified as having a dependency on the service; determining, at the at least a second chat channel, the outage is causing an incident with the at least a second service, wherein the determining comprises monitoring the at least a second chat channel for messages related to the incident; and interjecting, within the at least a second chat channel, a message regarding the outage affecting the service.

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 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 computer-implemented method for accurately identifying related incidents using textual data and contextual data includes receiving incident data associated with a computing system, wherein the incident data further comprises textual data and contextual data associated with the incident data. One or more relevant incidents associated with the received incident data is identified by applying an artificial intelligence model on the textual data associated with the received incident data. The identified one or more relevant incidents associated with the received incident data is provided to a site engineer device and the provided one or more relevant incidents is resolved.

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: Generating insight on a set of data is provided. A request for information regarding a specific topic is received from a client device corresponding to a requester. An analysis is performed on the request and a type of the information requested is determined based on the analysis. A set of information vendors is selected from a plurality of known information vendors based on the type of the information requested and other factors. Insights on the type of the information requested are obtained from the selected set of information vendors and an analysis is performed on the insights. A response to the request is generated based on the analysis of the insights on the type of the information requested that was obtained from the selected set of information vendors. The response to the request is sent to the client device corresponding to the requester.

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 includes a memory having instructions therein and at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to determine a global-level importance magnitude value for a global-level importance of an explainable feature of a machine learning base model based on a first prediction of the machine learning base model. The at least one processor is also configured to execute the instructions to determine a global-level importance direction label for the global-level importance of the explainable feature based on the first prediction. The at least one processor is also configured to execute the instructions to generate a communication for presentation to a user based on a second prediction of the machine learning base model, based on the global-level importance magnitude value, and based on the global-level importance direction label.

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: An enhanced information retrieval system takes a customer utterance and constructs a contextually-enriched content-based query allowing the system to retrieve the most relevant documents to assist an agent in a real-time conversation with the customer. Phrases in the utterance are classified as informational or non-informational using a machine learning system trained with phrases from prior conversations of multiple users. Content phrases are extracted from the informational phrases using keyword extraction (ranking noun phrases), intent/action extraction (semantic role labeling), and topic label extraction (clustering of historical logs). Emotional content is identified using a sequence tagging model and removed. Contextual information from prior conversations with this user is combined with the updated content phrases to create the contextually-enhanced content-based query, which can then be submitted to the information retrieval system.

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: An embodiment of the invention may include a method, computer program product and computer system for neural activity interpretation. The method, computer program product and computer system may include a computing device that presents a first user with a stimulus and monitors and maps the neural activity of the first user. The computing device may receive the first user's verbal reaction to the stimulus and map the linguistic data of the first user's verbal reaction to form a high dimensional vector based on the relationships of the mapped neural activity and the mapped verbal reaction of the first user. The computing device may associate the high dimensional vector with the stimulus presented resulting in a thoughts model. The computing device may receive a second user's neural activity and compare that second user's neural activity to the thoughts model to identify the neural activity in the second user.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for neural activity interpretation. The method, computer program product and computer system may include a computing device that presents a first user with a stimulus and monitors and maps the neural activity of the first user. The computing device may receive the first user's verbal reaction to the stimulus and map the linguistic data of the first user's verbal reaction to form a high dimensional vector based on the relationships of the mapped neural activity and the mapped verbal reaction of the first user. The computing device may associate the high dimensional vector with the stimulus presented resulting in a thoughts model. The computing device may receive a second user's neural activity and compare that second user's neural activity to the thoughts model to identify the neural activity in the second user.

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: Embodiments provide a computer implemented method of evaluating one or more IR systems, the method including: providing, by a processor, a pre-indexed knowledge-based document to a pre-trained sentence identification model; identifying, by the sentence identification model, a predetermined number of query-worthy sentences from the pre-indexed knowledge-based document, wherein the query-worthy sentences are ranked based on a prediction probability value of each query-worthy sentence; providing, by the sentence identification model, the query-worthy sentences to a pre-trained query generation model; generating, by the query generation model, a query for each query-worthy sentence; and evaluating, by the processor, the one or more IR systems using the generated queries, wherein one or more searches are performed via the one or more IR systems, and the one or more searches are performed in a set of knowledge-based documents including the pre-indexed knowledge-based document.

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.