Patent application EP17176522A (EP), filed 2017-06-19
Publication number: EP3260996A1
Title: DIALOGUE ACT ESTIMATION METHOD, DIALOGUE ACT ESTIMATION APPARATUS, AND STORAGE MEDIUM
Abstract: [0001]    A dialogue act estimation method, in a dialogue act estimation apparatus, includes acquiring first training data indicating, in a mutually associated manner, text data of a first sentence that can be a current uttered sentence, text data of a second sentence that can be an uttered sentence immediately previous to the first sentence, speaker change information indicating whether a speaker of the first sentence is the same as a speaker of the second sentence, and dialogue act information indicating a class of the first sentence, learning an association between the current uttered sentence and the dialogue act information by applying the first training data to a model, and storing a result of the learning as learning result information in a memory.
IPC codes: G06F  17/    27            A I
G06F  17/    30            A I
Claims: number: 1
text: A dialogue act estimation method, in a dialogue act estimation apparatus, comprising:
number: 2
text: The dialogue act estimation method according to claim 1, wherein the first training data further includes first speaker identification information indicating the speaker of the first sentence and second speaker identification information indicating the speaker of the second sentence.
number: 3
text: The dialogue act estimation method according to claim 2, wherein the model includes
a first model that outputs a first feature vector based on the text data of the first sentence, the text data of the second sentence, the first speaker identification information, the second speaker identification information, and a first weight parameter,
a second model that outputs a second feature vector based on the text data of the first sentence, the text data of the second sentence, the first speaker change information, and a second weight parameter, and
a third model that outputs a posterior probability of a dialogue act corresponding to the first sentence based on the first feature vector, the second feature vector, and a third weight parameter,
wherein the learning is performed by performing error backpropagation between the posterior probability and the dialogue act information indicated by the first training data.
number: 4
text: The dialogue act estimation method according to claim 3, wherein
the first model determines the first feature vector from the first sentence and the second sentence according to a first RNN-LSTM (Recurrent Neural Network-Long Short Term Memory) having the first weight parameter dependent on the first speaker identification information and the second speaker identification information, and
the second model determines the second feature vector from the first sentence and the second sentence according to a second RNN-LSTM having the second weight parameter dependent on first speaker change information.
number: 5
text: The dialogue act estimation method according to any of claims 1 to 4, wherein the first training data is acquired from a corpus in which two or more pieces of training data are accumulated.
number: 6
text: The dialogue act estimation method according to any of claims 1 to 5, further comprising
acquiring dialogue data including text data of a third sentence of a current uttered sentence uttered by a user, text data of a fourth sentence of an uttered sentence immediately previous to the third sentence, and second speaker change information indicating whether the speaker of the third sentence is the same as a speaker of the fourth sentence, and
estimating a dialogue act to which the third sentence is classified by applying the dialogue data to the model based on the learning result information.
number: 7
text: The dialogue act estimation method according to claim 1, wherein at least one of the acquiring, the learning, and the storing is performed by a processor.
number: 8
text: A dialogue act estimation apparatus comprising:
number: 9
text: The dialogue act estimation apparatus according to claim 8, wherein at least one of the acquisition unit, the learner, and the storage includes a processor.
number: 10
text: A non-transitory storage medium storing a computer-readable program, the program causing a computer to execute a process including:
Office action: search_report, dated 2017-12-27
Rejection reasons: novelty_epc_54
inventive_step_epc_56
Prior art cited: ref_id: JP3016779B1
ref_type: patent
severity: background
ref_id: JP3059413B2
ref_type: patent
severity: background
ref_id: JP5591772B2
ref_type: patent
severity: background
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severity: background
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ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: - NAL KALCHBRENNER ET AL: "Recurrent Convolutional Neural Networks for Discourse Compositionality", PROCEEDINGS OF THE WORKSHOP ON CONTINUOUS VECTOR SPACE MODELS AND THEIR COMPOSITIONALITY, 9 August 2013 (2013-08-09), pages 119 - 126, XP055413050
ref_type: npl
severity: background
ref_id: - SHENG-SYUN SHEN ET AL: "Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 1 April 2016 (2016-04-01), XP080692652
ref_type: npl
severity: background
ref_id: - Samira Ben DbabisHatem GhorbelLamia Hadrich BelguithMohamed Kallel, "Automatic Dialogue Act Annotation within Arabic Debates", Samira Ben DbabisHatem GhorbelLamia Hadrich BelguithMohamed Kallel, Computational Linguistics and Intelligent Text Processing, (20150414), page 478, 467, doi:10.1007/978-3-319-18111-0_35, ISSN 0302-9743, ISBN 978-3-642-17571-8, XP047311569 [X] 1,2,5-10 * the whole document * [Y] 3,4
ref_type: npl
severity: novelty_destroying
ref_id: - NAL KALCHBRENNER ET AL, "Recurrent Convolutional Neural Networks for Discourse Compositionality", PROCEEDINGS OF THE WORKSHOP ON CONTINUOUS VECTOR SPACE MODELS AND THEIR COMPOSITIONALITY, (20130809), pages 119 - 126, XP055413050 [YD] 3,4 * the whole document *
ref_type: npl
severity: obviousness
Final disposition: pending (2017-12-27)