International Business Machines Corporation

Zhe Liu

Jalal U Mahmud

Rama Kalyani T Akkiraju

Anbang Xu

Yufan Guo

Peifeng Yin

Haibin Liu

Taiga Nakamura

Pritam S Gundecha

Xiaotong Liu

Amita Misra

Vibha Singhal Sinha

Pooja Aggarwal

Prateeti Mohapatra

Rupaningal Sarasi Sarangi Lalithsena

Yash Bhalgat

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.

Machine learning model error detection

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.

Identification of related incident retrieval based on textual and contextual data

A cognitive system (artificial intelligence) is optimized by assessing different data augmentation methods used to augment training data, and then training the system using a training set augmented by the best identified method. The augmentation methods are assessed by applying them to the same set of training data to generate different augmented training data sets. Respective instances of the cognitive system are trained with the augmented sets, and each instance is subjected to validation testing to assess its goodness. The validation testing can include multiple validation tests leading to component scores, and a combined validation score is computed as a weighted average of the component scores using respective weights for each validation test. The augmentation method corresponding to the instance having the highest combined validation score is selected as the optimum augmentation method for the particular cognitive system at hand.

Data augmentation for text-based AI applications

A method and a system for model training are provided. The method can include training a first classifier, a second classifier, and a third classifier with subsets of a labeled dataset. The method can also include predicting a pseudo labeled dataset from an unlabeled dataset using the first classifier, the second classifier, and the third classifier. The method further includes assigning a role to the first classifier, to the second classifier, and to the third classifier. The method can further include selecting a teaching sample dataset from the pseudo labeled dataset based on the role assigned to the third classifier, wherein the third classifier is assigned a role of a student. The method can also include retraining the third classifier with the teaching sample dataset in conjunction with a subset of the labeled dataset.

Model training using a teacher-student learning paradigm

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.

Knowledge-based information retrieval system evaluation

Methods, systems, and computer program products for automatically generating datasets by processing collaboration forums using artificial intelligence techniques are provided herein. A computer-implemented method includes obtaining conversational data from collaboration forum sources; classifying, using a first set of artificial intelligence techniques, at least a portion of the conversational data into categories based on designated applications; extracting information, pertaining to test case-related issues, from at least a portion of the classified data; verifying at least a portion of the extracted information by analyzing, using a second set of artificial intelligence techniques, portions of the conversational data attributed to multiple entities and related to the extracted information; generating, using the verified information, one or more datasets related to at least one of the test case-related issues for at least one of the designated applications; performing at least one automated action based on the one or more generated datasets.

Automatically generating datasets by processing collaboration forums using artificial intelligence techniques

One embodiment provides a method that includes receiving adjusted labeled data based on emotional tone factors. Words are analyzed using a tone latent Dirichlet allocation (T-LDA) model that models tone intensity using the emotional tone factors and integrating the adjusted labeled data. Representative words are provided for each emotional tone factor based on using the T-LDA model. The representative words are obtained using the T-LDA model based on determining posterior probabilities and adjusting the posterior probabilities based on an auxiliary topic.

Machine learning from tone analysis in online customer service

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.

Method and system for unlabeled data selection using failed case analysis

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.

System for fine-grained affective states understanding and prediction

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.

Method for fine-grained affective states understanding and prediction

One embodiment provides a method that includes crawling a network for raw data. Emotion metrics are refined for the raw data. Labels for the raw data using refined emotion metrics are received. Factor analysis is performed for labeled data to obtain emotional tone factors. Adjusted labeled data are received based on the emotional tone factors. Words are analyzed using a tone model using the emotional tone factors and integrating the adjusted labeled data. Representative words for each emotional tone factor are provided based on using the tone model.

A method, a computer program product, and an information handling system is provided for reducing cost and improving real-time performance for analyzing and representing customer care communication progression based on tone assessments by monitoring communications between a plurality of customers and a plurality of representatives to form a plurality of interaction histories. Analyzing a text form of the plurality of interaction histories according to a Tone Latent Dirichlet Allocation [T-LDA] model to determine a plurality of conversation tones. Mapping the plurality of conversation tones to a plurality of conversation phases and providing a representation of the conversation tones identifying a first customer tone at a first conversation phase and a second customer tone at a second conversation phase.

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San Jose, CA, United States of America

Zhe Liu