National Institute of Information and Communications Technology, Incorporated

Julien Kloetzer

Kentaro Torisawa

Jonghoon Oh

Ryu Iida

Canasai Kruengkrai

Takuya Kawada

Jun Goto

Chikara Hashimoto

Masahiro Tanaka

Yoshihiko Asao

Junta Mizuno

Kazuma Kadowaki

A program for training a representation generator generating a representation representing an answer part included in a passage to classify whether the passage is related to an answer or not. The program causes a computer to operate as: a fake representation generator responsive to a question and a passage for outputting a fake representation representing an answer part of the passage; a real representation generator for outputting, for the question and a core answer, a real representation representing the core answer, in the same format as fake representation; a discriminator for discriminating whether fake representation and real representation are a real or fake representation; and a generative adversarial network unit training the discriminator and fake representation generator through generative adversarial network such that error determination of fake representation is maximized and error determination of real representation is minimized.

Answer classifier and representation generator for question-answering system using GAN, and computer program for training the representation generator

A causality recognizing apparatus includes a candidate vector generating unit configured to receive a causality candidate for generating a candidate vector representing a word sequence forming the candidate; a context vector generating unit generating a context vector representing a context in which noun-phrases of cause and effect parts of the causality candidate appear; a binary pattern vector generating unit, an answer vector generating unit and a related passage vector generating unit, generating a word vector representing background knowledge for determining whether or not there is causality between the noun-phrase included in the cause part and the noun-phrase included in the effect part; and a multicolumn convolutional neural network learned in advance to receive these word vectors and to determine whether or not the causality candidate has causality.

Causality recognizing apparatus and computer program therefor

A question answering device includes: a general word vector converter converting a question and an answer to semantic vectors in accordance with general context; a general sentence level CNN, in response to similarities of semantic vectors between words in question and answer and to strength of causality between the words, for weighting each semantic vector to calculate sentence level representations of the question and the answer; a general passage level CNN, in response to similarity between sentence level representations of question and answer, and to strength of relation of vectors in the sentence level representations viewed from causality, for weighting the sentence level representation to calculate a passage level representation for the question and answer passage; and a classifier determining whether or not an answer is a correct answer, based on the similarities between outputs from CNNsand

Non-factoid question-answering device

A summary generating apparatus includes a text storage device storing text with information indicating a portion to be focused on; word vector converters vectorizing each word of the text and adding an element indicating whether the word is focused on or not to the vector and thereby converting the text to a word vector sequence; an LSTM implemented by a neural network performing sequence-to-sequence type conversion, pre-trained by machine learning to output, in response to each of the word vectors of the word vector sequence input in a prescribed order, a summary of the text consisting of the words represented by the word sequence; and input units inputting each of the word vectors of the word vector sequence in the prescribed order to the neural network.

Summary generating apparatus, summary generating method and computer program

An entailment pattern pair extension apparatusextends entailment pairs by generating an n-term entailment pair from an m-term entailment pair, where m and n are integers not smaller than 0 and satisfying m&#x3c;n. Each entailment pair includes a first language pattern and a second language pattern entailed by the first language pattern. Entailment pattern pair extension apparatusincludes: a generation rule storage unitstoring generation rules for generating n-term entailment pairs from m-term entailment pairs, and a binary pair adding unitreceiving m-term entailment pair, determining, for each generation rule stored in the generation rule storage unit, whether its condition is satisfied by the m-term pair, and if the condition is satisfied, applying a modification rule of the generation rule to each language pattern constituting the m-term entailment pair.

Entailment pair extension apparatus, computer program therefor and question-answering system

[Object] To provide a device assisting a user to easily generate, in relation to an issue of interest to the user, a question sentence guaranteed to have an answer of a certain accuracy or higher in a question-answering system. [Solution] A question sentence generating device is used with a question-answering system, and it includes: word receiving means for receiving a wordas a source for generating a question sentence; and question sentence generating databasecomprised of a plurality of entries for generating a question sentence. Each of the plurality of entries has a word as a key and includes an answer sentence pattern co-occurring with the word, used in the question-answering system. The question sentence generating device further includes a question sentence generating unit, searching the question sentence generating databasefor an answer sentence pattern using the wordreceived by the word receiving means as a key, for generating a question sentence from a retrieved answer sentence pattern and the received word

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Tokyo, Japan

Julien Kloetzer