Abstract: A model learning apparatus of the present invention has a question/answer-pair expansion unit and a translation-model learning unit. The question/answer-pair expansion unit generates expansion question/answer pairs by increasing the number of question/answer pairs associated with an index indicating that it sounds more like the character. The translation-model learning unit learns a translation model and a reverse translation model by using the expansion question/answer pairs. A response selecting apparatus of the present invention has a record unit, a document search unit, a score calculation unit, and a ranking unit. The record unit records question/answer pairs and the above described learned translation model. The score calculation unit obtains a translation likelihood which is a numerical value based on the probability of obtaining the answer from the input question and calculates a score of each of a search-result question/answer pair with respect to the input question.

Abstract: A generation apparatus 100 includes: an argumentative scheme adding unit 10 which adds an argumentative scheme with respect to pair data constituted by an input utterance and a counter utterance 121 that voices a negative opinion with respect to the input utterance and which generates argumentative scheme-added pair data 122; a generation model learning unit 20 which learns a generation model for generating a counter utterance from an input utterance in consideration of the argumentative scheme by using the argumentative scheme-added pair data 122 as learning data and which generates a learned counter utterance generation model 123; and a counter utterance generating unit 30 which acquires an input utterance of a user and a designated argumentative scheme and which outputs a counter utterance using the counter utterance generation model 123.

Abstract: An extended role play-based utterance set generation apparatus includes a first data store storing role play-based utterance sets and a second data store storing non-role-played utterance sets. The role play-based utterance sets include a first query and a role play-based response to the query. The non-role-played utterance sets include a second query and a non-role-played response to the query. The disclosed technology determines similarity between the role play-based response and the non-role-played response. Upon determining that the role play-based response is the same or similar to the non-role-played response, the disclosed technology generates an association between the role play-based response and the second query and extends the role play-based utterance sets in the first data store with the second query.

Abstract: In communication performed among multiple participants, at least one of a participant who will start speaking next and a timing thereof is estimated. An estimation apparatus includes a head motion information generation unit that acquires head motion information representing head motions of communication participants in a time segment corresponding to an end time of an utterance segment and synchronization information for head motions between the communication participants, and an estimation unit that estimates at least one of the speaker of the next utterance segment following the utterance segment and the next utterance start timing following the utterance segment based on the head motion information and the synchronization information for the head motions between the communication participants.

Abstract: An expanded utterance that is used to output a more appropriate output utterance for an utterance can be generated. An utterance sentence expansion device includes an expansion unit that inserts, for an utterance that is an utterance to be expanded that includes a noun and is morphologically analyzed in advance, by using information of an expansion dictionary, which includes higher-level categories of the noun, one or more higher-level categories of the expansion dictionary corresponding to the noun included in the utterance into a position before the noun of the utterance to generate an expanded utterance.

Abstract: It is possible to ask an appropriate question for digging an utterance of the other party in depth. An interrogative search unit estimates an estimated used interrogative with a text, which is an utterance sentence, as an input, by using a predetermined rule or an estimator that has already learned, the estimated used interrogative being an interrogative related to the text. A candidate utterance sentence generation unit generates each of candidate utterance sentences for the utterance sentence with the text as an input through automatic utterance generation. Based on each of the candidate utterance sentences and an estimation result of the estimated used interrogative, a ranking unit ranks the candidate utterance sentenced, based on the scored.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities and an expression unit that expresses the nonverbal information on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling the expression unit so as to express behavior corresponding to the text.

Abstract: An utterance pair for expansion necessary for outputting an appropriate output utterance for an input utterance can be acquired.

Abstract: It is intended to allow learning of a spoken sentence generation model for generating a spoken sentence which enables a discussion covering a wide range of topics. A discussion data storage unit 100 stores a plurality of discussion data sets each including a discussion spoken sentence indicating a discussion topic and a pair of an approving spoken sentence indicating approval for the discussion spoken sentence and a disapproving spoken sentence indicating disapproval for the discussion spoken sentence.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates nonverbal information that corresponds to feature quantities of voice or text on the basis of the feature quantities and a learned nonverbal information general model. The nonverbal information is information for controlling an expression unit that expresses behavior so that at least one of the number of times that the behavior is performed and the magnitude of the behavior correspond to the feature quantities.

Abstract: An argumentation structure can be automatically extended. A template creation unit 22 creates a template using a predetermined rewriting pattern by receiving, as an input, a proposition represented by each of nodes in an argumentation structure including the nodes respectively representing a main proposition of argumentation and a proposition that functions as an argument for another proposition. An additional proposition output unit 28 creates a proposition functioning as an argument for the input proposition using the created template and outputs the created proposition as a proposition represented by a node to be added to the argumentation structure.

Abstract: A communication skill evaluation system including: an audio input device; a measurement device; an utterance period detection unit that, based on the audio information input by the audio input device, detects an utterance period and a participant who spoke in each utterance period; a participatory role assignment unit that assigns a participatory role to each participant in accordance with whether or not each participant spoke in each utterance pair composed of two utterances obtained in chronological order from each utterance period; a feature quantity extraction unit that, based on the measurement results by the measurement device, extracts a non-verbal feature quantity for each participant, relating to the non-verbal action at the end of the utterance in each utterance period; and an estimation unit that estimates communication skill for each participant based on a combination, in each utterance period, of the participatory role and the non-verbal feature quantity.

Abstract: A response selecting apparatus includes a recording part, a document searching part, an information acquiring part, a score calculating part, and a ranking part. The document searching part searches for a question-answer pair from the question-answer pairs recorded in the recording part using the input question as input. The information acquiring part acquires information for quantifying appropriateness of the search-result-question-answer pair with respect to the input question using the input question and the search-result-question-answer pair as input. The score calculating part calculates a score with respect to the input question for each of the search-result-question-answer pairs from a numerical value indicating appropriateness based on a search scheme and a numerical value based on the quantification information.

Abstract: An extended role play-based utterance set generation apparatus includes a first storage part 1 in which I role play-based utterance sets i=(ti(1), ti(2)) (i=1, . . . , I) are stored, a second storage part 2 in which J non-role-played utterance sets j=(t?j(1), t?j(2)) (j=1, . . . , J) are stored, a non-role-played utterance set selecting part 3 configured to select from the second storage part 2, one or more non-role-played utterance sets j corresponding to the second utterance t?j(2) which is the same as or similar to the second utterance ti(2) of each of the role play-based utterance sets i read from the first storage part 1, and an extended role play-based utterance set generating part 4 configured to associate the first utterance t?j(1) of each of the selected non-role-played utterance sets j with the second utterance ti(2) of each of the role play-based utterance sets i to make an extended role play-based utterance set.

Abstract: A model learning apparatus of the present invention has a question/answer-pair expansion unit and a translation-model learning unit. The question/answer-pair expansion unit generates expansion question/answer pairs by increasing the number of question/answer pairs associated with an index indicating that it sounds more like the character. The translation-model learning unit learns a translation model and a reverse translation model by using the expansion question/answer pairs. A response selecting apparatus of the present invention has a record unit, a document search unit, a score calculation unit, and a ranking unit. The record unit records question/answer pairs and the above described learned translation model. The score calculation unit obtains a translation likelihood which is a numerical value based on the probability of obtaining the answer from the input question and calculates a score of each of a search-result question/answer pair with respect to the input question.

Abstract: A template creation unit 22 creates a template using a predetermined rewriting pattern by receiving, as an input, a proposition represented by each of nodes in an argumentation structure including the nodes respectively representing a main proposition of argumentation and a proposition that functions as an argument for another proposition. An additional proposition output unit 28 creates a proposition functioning as an argument for the input proposition using the created template and outputs the created proposition as a proposition represented by a node to be added to the argumentation structure.

Abstract: In communication performed among multiple participants, at least one of a participant who will start speaking next and a timing thereof is estimated. An estimation apparatus includes a head motion information generation unit that acquires head motion information representing head motions of communication participants in a time segment corresponding to an end time of an utterance segment and synchronization information for head motions between the communication participants, and an estimation unit that estimates at least one of the speaker of the next utterance segment following the utterance segment and the next utterance start timing following the utterance segment based on the head motion information and the synchronization information for the head motions between the communication participants.

Abstract: A nonverbal information generation apparatus includes a display unit that partitions text into predetermined units, displays the text partitioned into the predetermined units, and makes nonverbal information that represents information about behavior of a verbal output agent or nonverbal information that represents information about behavior of a receiver of verbal information of the verbal output agent that corresponds to the text when the verbal output agent outputs the verbal information visible in association with the predetermined units of the text.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling an expression unit that expresses behavior that corresponds to the text.

Abstract: A nonverbal information generation apparatus includes a nonverbal information generation unit that generates time-information-stamped nonverbal information that corresponds to time-information-stamped text feature quantities and an expression unit that expresses the nonverbal information on the basis of the time-information-stamped text feature quantities and a learned nonverbal information generation model. The time-information-stamped text feature quantities are configured to include feature quantities that have been extracted from text and time information representing times assigned to predetermined units of the text. The nonverbal information is information for controlling the expression unit so as to express behavior corresponding to the text.