Nippon Telegraph and Telephone Corporation

Ntt Corporation

Kosuke Nishida

Junji Tomita

Kyosuke Nishida

Hisako Asano

Itsumi Saito

Atsushi Otsuka

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.

Text generation apparatus, text generation learning apparatus, text generation method, text generation learning method and program

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.

An encoding unit transforms a piece of text divided into a plurality of spans that are subdivided units of the piece of text and a question which have been input into a vector representation sequence representing a meaning of a span and the question based on the piece of text and the question which have been input using a pre-trained encoding model for transforming input text into a vector representation sequence representing a meaning of the input text. For each of the spans, an evidence extraction unit estimates an evidence score indicating the degree to which the span is suitable as the evidence for extracting the answer using a pre-trained extraction model for calculating the evidence score based on the vector representation sequence.

Answer generating device, answer learning device, answer generating method, and answer generating program

A sentence generation device has: an estimation unit for receiving input of a first sentence and an output length, and estimating importance of each word constituting the first sentence using a pre-trained model; and a generation unit for generating a 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 output length.

Text generation apparatus, text generation method, text generation learning apparatus, text generation learning method and program

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.

Sentence generation using pre-trained model with word importance and focus point

There is provided a learning device for learning a neural network used for search of external knowledge in order to increase search accuracy of external knowledge required for arithmetic processing. With an input sentence Q as an input, an external knowledge search unitselects pieces of external knowledge based on similarity degrees between pieces of external knowledge included in an external knowledge databaseand the input sentence Q, using a neural network, and causes the selected pieces of external knowledge to be search results R. A processing unitacquires a response sentence A to the input sentence Q by arithmetic processing with the input sentence Q and the selected pieces of external knowledge as an input. A consideration calculation unitcalculates a consideration v determined from an index indicating correctness of the response sentence A based on a true output T given to the input sentence Q in advance and an index indicating quality of the selected pieces of external knowledge. A learning unitupdates neural network parameters of the second external knowledge search unitusing the consideration v.

Learning device, learning method and learning program

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.

Question responding apparatus, question responding method and program

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.

To make it possible to accurately generate a word vector even if vocabulary of a word vector data set is not limited. In a vector generating devicethat generates vectors representing an input sentence P, when generating a series of the vectors representing the input sentence P based on vectors corresponding to words included in the input sentence P, a definition-sentence-considered-context encode unitgenerates, based on a dictionary DBstoring sets of headwords y and definition sentences D, which are sentences defining the headwords y, concerning a word, which is the headword stored in the dictionary DB, among the words included in the input sentence P, the series of the vectors representing the input sentence P using the definition sentence Dof the headwords y.

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Tokyo, Japan

Kosuke Nishida