International Business Machines Corporation

Sara Rosenthal

Mihaela Ancuta Bornea

Radu Florian

Avirup Sil

Ashish Jagmohan

Krishna Chaitanya Ratakonda

Yi-Min Chee

Kenneth James Barker

Adam R Faulkner

Branimir Konstantinov Boguraev

Sugato Bagchi

Naga A Ayachitula

Abhishek Malvankar

Pei-Yun S Hsueh

Carlos A Fonseca

Siddharth A Patwardhan

Jeffrey Scott McCarley

Subhro Das

Lin Pan

Ching-Hua Chen

Chandramouli Maduri

Gema Almoguera

Yanpeng Li

A method of using a computing device using natural language software to respond to a Boolean question is presented. The method includes receiving, by a computing device, a question and at least one passage. The computing device classifies the question as a Boolean type question. The computing device generates evidence from the at least one passage to the Boolean question. The computing device generates a response to the Boolean question using the generated evidence. The computing device provides the generated evidence that supports the response to the Boolean question.

Computer assisted answering Boolean questions with evidence

A method, computer program product, and/or computer system generate a first adversarial statement via: (1) receiving a question and an original context for the question; (2) converting the question into a statement with a placeholder answer; (3) picking randomly an answer entity from a training text corpus; (4) replacing the placeholder answer with the randomly picked answer entity; and (5) leaving a correct question entity in the statement. The first adversarial statement is inserted into the original context to form a first adversarial context. The question and the first adversarial context as a first pair and the question and the original context as a second pair are input into a question-answer dialog system to train the question-answer dialog system.

Training a question-answer dialog system to avoid adversarial attacks

A method includes receiving, by a question-answer system, a question in a first language and the question in a second language and predicting, by the question-answer system, a first answer to the question in the first language and a second answer to the question in the second language. The method also includes generating, by the question-answer system, a first vector representing the question in the first language and a second vector representing the question in the second language and adjusting the question-answer system based on the first and second answers and the first and second vectors such that when the question-answer system subsequently generates a third vector representing the question in the first language and a fourth vector representing the question in the second language, a distance between the third and fourth vectors is less than a distance between the first and second vectors.

Enhancing multi-lingual embeddings for cross-lingual question-answer system

Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Intelligent recommendations for program code

A mechanism is provided in a data processing system to implement a personalized patient engagement engine. The personalized patient engagement engine develops a set of models for a plurality of behavioral phenotypes based on anonymized unstructured and structured patient-care management records for a plurality of patients over a period of time; matches a given patient to a behavioral phenotype; estimates a propensity of positive and/or negative behavioral responses of each of a plurality of targeted behaviors; dynamically updates personalized intervention effectiveness rankings in context for care manager and patient decision-making based on what has been shown to lead to positive responses for individuals with a similar behavioral profile; generates an intervention recommendation for the given patient based on the personalized intervention effectiveness rankings relative to the patient given an assigned goal and an individual intervention effect estimation; and provides the intervention recommendation to the care manager.

Personalized patient engagement in care management using explainable behavioral phenotypes

A method, computer program product, and/or computer system protects a question-answer dialog system from being attacked by adversarial statements that incorrectly answer a question. A computing device accesses a plurality of adversarial statements that are capable of making an adversarial attack on a question-answer dialog system, which is trained to provide a correct answer to a specific type of question. The computing device utilizes the plurality of adversarial statements to train a machine learning model for the question-answer dialog system. The computing device then reinforces the trained machine learning model by bootstrapping adversarial policies that identify multiple types of adversarial statements onto the trained machine learning model. The computing device then utilizes the trained and bootstrapped machine learning model to avoid adversarial attacks when responding to questions submitted to the question-answer dialog system.

Training a question-answer dialog sytem to avoid adversarial attacks

Provided herein are techniques for processing a context-situated inquiry to provide results satisfying the inquiry. An inquiry and its supporting context are processed using natural language processing to determine an interpretation for the inquiry and context, and the interpretation is presented to receive corrective information for the interpretation. The corrective information is applied to the interpretation and content is retrieved based on the interpretation of inquiry and context to produce candidate results for the inquiry. Supplemental information is iteratively received to generate updated candidate results.

Processing context-based inquiries for knowledge retrieval

A method for analyzing a virtual discussion. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

Computer-based analysis of virtual discussions for products and services

A method for analyzing a virtual discussion is provided. The method may include identifying, with a processing device, a first concept relevant to a first subdiscussion associated with an online discussion. The method may also include identifying a second concept relevant to the first subdiscussion, and determining a relation between the first concept and the second concept.

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Spring Valley, NY, United States of America

Sara Rosenthal