Abstract: There is provided an information processing to implement highly efficient and highly accurate feedback with the calculation amount suppressed. The information processing device includes a processing unit that executes processing using a neural network, in which the processing unit performs feedback related to a plurality of function parameters respectively used in a plurality of intermediate layers in the neural network on the basis of an output from the intermediate layers.

Abstract: An information processing apparatus according to an embodiment of the present technology includes a generation unit, an evaluation unit, and an update unit. The generation unit generates input data on the basis of a predetermined parameter. The evaluation unit generates evaluation data on the basis of first output data that includes evaluation target data and is output by inputting first input data generated by the generation unit to a first recognition model, and second output data that includes a pseudo label as a pseudo correct answer of the evaluation target data and is output by inputting second input data generated by the generation unit to a second recognition model. The update unit updates the predetermined parameter on the basis of the evaluation data.

Abstract: A sentence generation device has: an estimation unit for receiving input of a first sentence and a focus point related to generation of a second sentence to be generated based on the first sentence, and estimating importance of each word constituting the first sentence using a pre-trained model; and a generation unit for generating the second sentence based on the importance, and thus makes it possible to evaluate importance of a constituent element of an input sentence in correspondence with a designated focus point.

Abstract: An information processing apparatus includes processing circuitry configured to receive an analysis target sentence as an input and generate a generation sentence, the generation sentence being a sentence regarding content of the analysis target sentence, and a classification type, the classification type being information indicating whether or not the generation sentence is a question sentence for information in which an answer is not included in the analysis target sentence, using a machine learning model learned in advance, and output the generation sentence and the classification type generated.

Abstract: A dialogue device includes: an initial utterance generation unit that generates first utterance information serving as a trigger for a dialogue with a user; a general-purpose dialogue generation unit that generates second utterance information; and a dialogue control unit that controls the dialogue with the user, based on the first utterance information or the second utterance information.

Abstract: To make it possible to generate dialogue data for generating a question sentence to deeply delve conversation at a low cost. For each of a plurality of pieces of data each including a set of a first utterance sentence that is a sentence uttered by a first user, a second utterance sentence that is a sentence uttered by a second user and is a response to the first utterance sentence, and a third utterance sentence that is a sentence uttered by the first user and is a response to the second utterance sentence, a dialogue data generation unit 110 generates a set of the first utterance sentence of the data and the second utterance sentence of the data as dialogue data when the second utterance sentence of the data is a question sentence using an interrogative.

Abstract: A text generation apparatus includes a content selection unit that acquires a reference text based on an input text and information different from the input text and a generation unit that generates a text based on the input text and the reference text, wherein the content selection unit and the generation unit are neural networks based on learned parameters, so that information to be considered when generating a text can be added as text.

Abstract: It is possible to ask an appropriate question for digging an utterance of the other party in depth. An interrogative search unit estimates an estimated used interrogative with a text, which is an utterance sentence, as an input, by using a predetermined rule or an estimator that has already learned, the estimated used interrogative being an interrogative related to the text. A candidate utterance sentence generation unit generates each of candidate utterance sentences for the utterance sentence with the text as an input through automatic utterance generation. Based on each of the candidate utterance sentences and an estimation result of the estimated used interrogative, a ranking unit ranks the candidate utterance sentenced, based on the scored.

Abstract: A question generation device includes: generating means which uses a query and a relevant document including an answer to the query as input and, using a machine learning model having been learned in advance, generates a revised query in which a potentially defective portion of the query is supplemented with a word included in a prescribed lexical set.

Abstract: A text generation apparatus includes a memory and a processor configured to execute acquiring a reference text based on an input text and information different from the input text; and generating a text based on the input text and the reference text, wherein the acquiring and the generating are implemented as neural networks based on learned parameters.

Abstract: An opinion aggregation apparatus 100 includes: a first determining unit 10 which determines whether an input sentence is a declarative sentence or an interrogative sentence; a first generating unit 20 which generates, when the input sentence is the declarative sentence, first text data being the input sentence converted into an interrogative sentence; a second generating unit 30 which generates, when the input sentence is the interrogative sentence, second text data being a simple answer to the input sentence; a storage unit 120 which stores a chat text database including a plurality of pieces of chat text data; a calculating unit 40 which calculates a first score indicating sentence continuity between the first text data and the chat text data or a second score indicating sentence continuity between the chat text data and the second text data; and a second determining unit 50 which outputs, when the first score or the second score is equal to or higher than a threshold, the chat text data with the first scor

Abstract: This disclosure is provided, in which an answer generation unit configured to receive a document and a question as inputs, and execute processing of generating an answer sentence for the question by a learned model by using a word included in a union of a predetermined first vocabulary and a second vocabulary composed of words included in the document and the question, in which the learned model includes a learned neural network that has been learned in advance whether word included in the answer sentence is included in the second vocabulary, and increases or decreases a probability at which a word included in the second vocabulary is selected as the word included in the answer sentence at the time of generating the answer sentence by the learned neural network.

Abstract: To enable provision of appropriate information for a user query even in a case there are multiple information provision modules which are different in answer generation processing. A query sending unit 212 sends a user query to each one of a plurality of information provision module units 220 that are different in the answer generation processing and that each generate an answer candidate for the user query. An output control unit 214 performs control such that the answer candidate acquired from each one of the plurality of information provision module units 220 is displayed on a display unit 300 on a per-agent basis with information on an agent associated with that information provision module unit 220.

Abstract: Provided are a dialogue device, a dialogue method, a data structure, and a program capable of realizing various dialogues while reducing the amount of description of a dialogue scenario. A knowledge transition unit 120 determines a next type of knowledge based on: a knowledge base 130 in which a relation label indicating each of relations between a plurality of types of knowledge is attached to knowledge about each of utterances to express the knowledge about the utterance; a user utterance; current knowledge; and a dialogue scenario including a basic scenario in which a transition method between the plurality of types of knowledge in the knowledge base is determined using the relation label, and an utterance generation unit 150 generates a system utterance based on the next type of knowledge.

Abstract: A text generation apparatus includes a memory and a processor configured to execute acquiring a reference text based on an input text and information different from the input text; and generating a text based on the input text and the reference text, wherein the acquiring and the generating are implemented as neural networks based on learned parameters.

Abstract: A generation apparatus includes a generation unit configured to use a machine learning model learned in advance, with a document as an input, to extract one or more ranges that are likely to be answers in the document and generate a question representation whose answer is each of the ranges that are extracted.

Abstract: A text generation apparatus includes a memory and a processor configured to, based on learned parameters of neural networks, acquire, as a reference text, a predetermined number of two or more sentences having a relatively high relevance to an input sentence from a set of sentences different from the input sentence and generate text based on the input sentence and the reference text, such that information to be considered when generating text can be added as text.

Abstract: An information processing device includes a processing unit configured to receive as input a document and a question, and to execute processing to output an answer range as a range of a string that can be an answer to the question in the document, or an answer suitability of the document with respect to the question, by using neural networks, wherein the processing unit includes a first neural network configured to calculate the answer range, and a second neural network configured to calculate the answer suitability, and between the first neural network and the second neural network, part of layers constituting both neural networks is shared.

Abstract: It is possible to ask an appropriate question for digging an utterance of the other party in depth. An interrogative search unit estimates an estimated used interrogative with a text, which is an utterance sentence, as an input, by using a predetermined rule or an estimator that has already learned, the estimated used interrogative being an interrogative related to the text. A candidate utterance sentence generation unit generates each of candidate utterance sentences for the utterance sentence with the text as an input through automatic utterance generation. Based on each of the candidate utterance sentences and an estimation result of the estimated used interrogative, a ranking unit ranks the candidate utterance sentenced, based on the scored.

Abstract: A generation apparatus includes a generation unit configured to use a machine learning model learned in advance, with a document as an input, to generate a question representation for a range of an answer in the document, wherein when generating a word of the question representation by performing a copy from the document, the generation unit adjusts a probability that a word included in the range is copied.