A computer-implemented method for predicting knowledge elements in answer to requests for information, and an associated system that processes the information request (1) made using an intermediate predictive model (5) and a knowledge element prediction fit model (8) to generate a set of fitted knowledge elements to prepare an answer, associated with their respective probabilities of use (12), as a suggestion for the preparation of an answer to an information request (15). The suggested knowledge elements are corrected and/or updated to prepare answers based on historical data, such as: data predicted by the intermediate predictive model (5), answers sent to requesters (15), contents of one or more knowledge elements used in the answers (17) and/or feedback data on the relevance of the answers (21) sent.

This application relates to a new paradigm of data reasoning based on natural language inputs. A data structure based on the new paradigm is a canonical of a basic data structure in the shape of a triangle. The basic data structure includes a first node being a primary node, a second node being a context node, a third node being a resultant node. The basic data structure also includes a first link connecting the first and second nodes and configured to assign an attribute of abductive reasoning between the first and second nodes and a second link connecting the second and third nodes and configured to assign an attribute of inductive reasoning between the second and third nodes. The basic data structure further includes a third link connecting the first and third nodes and configured to assign an attribute of deductive reasoning between the first and third nodes.

Embodiments of the present disclosure are directed to a method and an apparatus for generating training data for a visual question answering (VQA) system, and a computer readable medium. The method for generating training data for a visual question answering system includes: obtaining a first set of training data of the visual question answering system, the first set of training data comprising a first question for an image in the visual question answering system and a first answer corresponding to the first question; obtaining information related to the image; generating a second question corresponding to the first answer based on the information to obtain a second set of training data for the image in the visual question answering system, the second set of training data comprising the second question and the first answer.

An approach is provided that receives a question at a question-answering (QA) system. A number of passages are identified that are relevant to the received question. A question knowledge graph is generated that corresponds to the question and a set of passage knowledge graphs are also generated with each passage knowledge graph corresponding to one of the identified passages. Each of the passage knowledge graphs are compared to the question knowledge graph with the comparison resulting in a set of knowledge graph candidate answers (kgCAs). A set of candidate answers (CAs) is computed by the QA with at least one of the CAs being based on one of the kgCAs.

A method includes generating a content entigen group for content using identigen pairing rules. The method further includes obtaining a contradicting entigen group from a knowledge database based on the content entigen group. The method further includes updating the knowledge database to include the content entigen group. A contradicted entigen of the content entigen group is established to indicate duplication of a contradicted entigen of the contradicting entigen group.

Generating questions by receiving user utterance data, determining an intent confidence vector for the user utterance data, predicting, by a trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector, and generating a next question using the next user-intent confidence vector.

Generating automated conversation responses by receiving a conversation input message, determining an intent associated with the conversation input message, detecting content associated with the intent in a data stream in response to determining the intent, and generating a conversation output according to the content and the intent.

A system and corresponding method are provided for generating responses for a dialogue between a user and a computer. The system includes a memory storing information for a dialogue history and a knowledge base. An encoder may receive a new utterance from the user and generate a global memory pointer used for filtering the knowledge base information in the memory. A decoder may generate at least one local memory pointer and a sketch response for the new utterance. The sketch response includes at least one sketch tag to be replaced by knowledge base information from the memory. The system generates the dialogue computer response using the local memory pointer to select a word from the filtered knowledge base information to replace the at least one sketch tag in the sketch response.

A question-and-answer system directed to a specific domain optimally utilizes reference documents that are semantically complete for that domain. Semantic completeness of a document is assessed using quality control questions (provided by subject matter experts) applied to the Q&A system followed by analysis of the proposed answers. That analysis is carried out using a cogency module having a feedforward neural network which receives metadata features of the document such as document ownership, document priority, and document type. A domain-optimized corpus for the Q&A system is built by so assessing multiple documents in a document collection, and adding each reference document that is reported as being semantically complete to the domain-optimized corpus. Thereafter, the deep learning question-and-answer system can receive a natural language query from a user, find a responsive answer in the documents while applying the domain-optimized corpus, and provide that answer to the user.

A learning data generation device for generating learning data for learning a recognizer capable of estimating a contour of a sphere making spinning motion, with high accuracy, the sphere being recorded in a single camera video image, is provided. The learning data generation device includes: a spinning rate estimation unit that receives an input of a learning video image in which motion of a spinning sphere is recorded and an initial value of a size of a contour of the recorded sphere in the video image, sets a plurality of set values of the size of the contour based on the initial value, and obtains an estimated value of a spinning rate of the sphere based on the learning video image, for each of the set values; a contour determination unit that receives an input of a true value of the spinning rate of the sphere, the true value being obtained in advance for the learning video image, and determines at least any of a plurality of the set values respectively corresponding to a plurality of the estimated values selected in order of closeness to the true value, as a determined value of the contour; and a learning data output unit that outputs the learning video image and the determined value as learning data.