National Institute of Information and Communications Technology, Incorporated

Honda Motor Co., Ltd.

Ryu Iida

Kentaro Torisawa

Jonghoon Oh

Canasai Kruengkrai

Yutaka Kidawara

Julien Kloetzer

Kanako Onishi

Yoshihiko Asao

Chikara Hashimoto

Kotaro Funakoshi

Mikio Nakano

Kiyonori Ootake

Junta Mizuno

Takenobu Tokunaga

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 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 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

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.

Dialogue system and computer program therefor

An annotation data generation assisting system includes: an input/output device receiving an input through an interactive process; morphological analysis systemand dependency parsing system performing morphological and dependency parsing on text data in text archive; first to fourth candidate generating units detecting a zero anaphor or a referring expression in the dependency relation of a predicate in a sequence of morphemes, identifying a position as an object of annotation and estimating candidates of expressions to be inserted by using language knowledge; a candidate DB storing estimated candidates; and an interactive annotation device reading candidates of annotation from candidate DB and annotate a candidate selected by an interactive process by input/output device.

Annotation assisting apparatus and computer program therefor

A referring expression processor which uses a probabilistic model and in which referring expressions including descriptive, anaphoric and deictic expressions are understood and generated in the course of dialogue is provided. The referring expression processor according to the present invention includes: a referring expression processing section which performs at least one of understanding and generation of referring expressions using a probabilistic model constructed with a referring expression Bayesian network, each referring expression Bayesian network representing relationships between a reference domain (D) which is a set of possible referents, a referent (X) in the reference domain, a concept (C) concerning the referent and a word (W) which represents the concept; and a memory which stores data necessary for constructing the referring expression Bayesian network.

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Tokyo, Japan

Ryu Iida