Abstract: 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.

Abstract: A neural architecture search method, system, and computer program product that determines, by a computing device, a best fit language model of a plurality of language models that is a best fit for interpretation of a corpus of natural language and interprets, by the computing device, the corpus of natural language using the best fit language model.

Abstract: A method and system are provided for combining models. The method includes forming, by a computer having a processor and a memory, model pairs from a model ensemble that includes a plurality of models. The method further includes comparing the model pairs based on sets of output results produced by the model pairs to provide comparison results. The method also includes constructing, by the computer, a combination model from at least one of the model pairs based on the comparison results. The comparing step is performed using user-generated set-based feedback.

Abstract: 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.

Abstract: Receive a question via a graphical user interface (GUI), obtain a passage of text potentially relevant to the question, and receive, via the GUI, a selection of a number of question-answering models to be ensembled. Produce a plurality of answers to the question by running a plurality of question-answering models, consistent with the selection of the number of question-answering models to be ensembled, on the passage of text. Produce an ensembled answer by ensembling the plurality of answers according to their respective confidence scores. Display, via the GUI, the ensembled answer in context of the passage of text, with the ensembled answer visually marked in the passage of text. Optionally, repeat these steps for a second passage of text.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method according to one embodiment includes converting an input question into a vector form using trained word embeddings; constructing a type similarity matrix using a predetermined ontology; and determining a score for all possible types for the input question, based on the input question in vector form and the type similarity matrix.

Abstract: A computer-implemented method of performing text-to-text transformation includes performing a text transformation operation on an original input text of a specific task to generate a plurality of transformed text. A task-specific performance metric that measures an operation of the specific task is applied to each one of the plurality of transformed text. Each of the plurality of transformed text are paired with the task-specific performance metric. A training dataset is updated to include each pairing of the plurality of transformed text with the task-specific metric.

Abstract: 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.

Abstract: A method of using a computing device to improve an answer generated by a natural language question and answer system includes receiving, by a computing device, multiple questions in a natural language question and answer system. The computing device further generates multiple answers to the multiple questions. The computing device still further constructs a new training set with the generated multiple answers, where each answer is compared with a corresponding question of the multiple questions. The computing device additionally augments the new training set with one or more tokens delimiting a span of one or more of the generated multiple answers. The computing device further trains a new natural language question and answer system with the augmented new training set.

Abstract: 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.

Abstract: A computer-implemented method according to one embodiment includes converting an input question into a vector form using trained word embeddings; constructing a type similarity matrix using a predetermined ontology; and determining a score for all possible types for the input question, based on the input question in vector form and the type similarity matrix.

Abstract: A personalized scheduling and networking method, system, and non-transitory computer readable medium include creating a user persona profile based on user information, extracting scheduling and networking information based on event information, matching a user with events during a conference based on the extracted scheduling and networking information and the user persona profile, and creating a schedule and a route for maximizing a number of the events and networking opportunities based on matches of the matching.

Abstract: A roadway condition predictive model that can recommend ameliorative roadway action(s) is disclosed. A roadway controller computer acquires sensor and location information, which describe physical driving surface conditions on the roadway, from a plurality of vehicles that travel on the roadway. The roadway controller computer also acquires environmental and traffic conditions for the roadway. The roadway controller computer creates a predictive model that describes a deterioration rate and future physical condition of the roadway, based on the acquired sensor, location information, environmental and traffic conditions for the roadway. The roadway controller computer then implements an action to ameliorate deterioration of the roadway that is predicted by the predictive model.

Abstract: A computer-implemented computer documentation validation method, the method comprising: manipulating a user interface of an operating system by taking control of a user input device to execute a command of a computer software documentation on behalf of the user; and outputting an error code when a failure is a result of the executed command.

Abstract: A query expansion method, system, and computer program product, include establishing a context of a query to execute the query within a search index by labeling phrases of interest of the query, expanding of the phrases of interest based on a language model and a topic model, and identifying and incorporating an available historical query into the context according to a historical phrase of interest and an expansion of one or more historical phrases of interest, and retrieving and displaying one or more search results based on the query and the context of the query as a first visual mark indicating a linkage between one or more terms of the query and the one or more search results and a second visual mark indicating an alignment between the available historical query and a match to the terms of the query and the search result.

Abstract: An analytics engine operates on data associated with users and opportunities. A broker system provides the opportunity data to the analytics engine, but the analytics engine does not provide user data to the broker system. The analytics engine notifies users of results of analytics operations.

Abstract: A power tool accident prevention system receiving images from a static camera of a setup of a power tool, the system comprising: a processor; and a memory, the memory storing instructions to cause the processor to: analyze the images to identify inherent dangers in the setup of the power tool; identify at least one potential cause of an accident based on the identified inherent dangers; and activate an emergency safety measure of the power tool to avoid the at least one potential cause of the accident.

Abstract: A computer-implemented computer documentation validation method, the method comprising: manipulating a user interface of an operating system by taking control of a user input device to execute a command of a computer software documentation on behalf of the user; and outputting an error code when a failure is a result of the executed command.