National Institute of Information and Communications Technology, Incorporated

Canasai Kruengkrai

Kentaro Torisawa

Jonghoon Oh

Ryu Iida

Yutaka Kidawara

Julien Kloetzer

Kanako Onishi

Yoshihiko Asao

Chikara Hashimoto

Masahiro Tanaka

Junta Mizuno

A request paraphrasing systemallowing a dialogue system to flexibly address to requests in various different manners of expression includes: a pre-processing unitconverting a user inputto a word vector sequence; and a neural paraphrasing modeltrained in advance by machine learning to receive the word vector sequence as an input and paraphrasing a request represented by the word vector sequence to a request having a higher probability of obtaining an answer from a question-answering devicethan the request before paraphrasing. As pre-processing, whether the user inputis a request or not may be determined and it may be paraphrased only when it is determined to be a request. Further, a classification modelmay classify the input request to determine to which request class it belongs, and the classification may be input as one feature to neural paraphrasing model

Request paraphrasing system, request paraphrasing model and request determining model training method, and dialogue system

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 dialogue system includes a response utterance selecting unit generating a response utterance original sentence to an input utterance; an input utterance emotion estimating unit estimating emotion of the input utterance by calculating input utterance emotion scores indicating degree of matching between the emotion represented by the input utterance and a plurality of emotions; and a response utterance modifying unit for calculating response utterance emotion scores of a response utterance original sentence as emotion scores for each of the plurality of emotions, modifying the response utterance original sentence by a method of modification determined by the values of input utterance emotion score and the response utterance emotion score, and thereby generating and outputting a response utterance.

Dialogue system and computer program therefor

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

A dialogue system includes: a question generating unit receiving an input sentence from a user and generating a question using an expression included in the input sentence, by using a dependency relation; an answer obtaining unit inputting the question generated by the question generating unit to a question-answering system and obtaining an answer to the question from question-answering system; and an utterance generating unit for generating an output sentence to the input sentence, based on the answer obtained by the answer obtaining unit.

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Tokyo, Japan

Canasai Kruengkrai