Abstract: This disclosure relates to a method of generating a symbol string based on an input sentence represented by a sequence of symbols. In particular, the method involves receiving an input symbol string representing a sentence, generating, using a neural network based on a sequence of dependency structure of elements in the input symbol string, an output symbol string corresponding to the input sentence. The neural network includes an encoder that converts elements of the input symbol string to a first hidden state in a form of a multi-dimensional vector, an attention mechanism that applies a weight to the first hidden state and generates the weighted first hidden state as a second hidden state, a decoder that generates a third hidden state based on at least one element of the input symbol string, an element of the output symbol string, and the second hidden state, and an output generator that generates an element of the output symbol string based on the second hidden state and the third hidden state.

Abstract: An alignment device includes a memory and a processor configured to execute generating a span prediction problem between first domain sequence information and second domain sequence information by receiving the first domain sequence information and the second domain sequence information as inputs; and using a span prediction model created using data including a cross-domain span prediction problem and an answer to the span prediction problem, and predicting a span to be an answer to the span prediction problem.

Abstract: A speech recognition unit converts an input utterance sequence into a confusion network sequence constituted by a k-best of candidate words of speech recognition results; a lattice generating unit generates a lattice sequence having the candidate words as internal nodes and a combination of k words among the candidate words for an identical speech as an external node, in which edges are extended between internal nodes other than internal nodes included in an identical external node, from the confusion network sequence; an integer programming problem generating unit generates an integer programming problem for selecting a path that maximizes an objective function including at least a coverage score of an important word, of paths following the internal nodes with the edges extended, in the lattice sequence; and the summary generating unit generates a high-quality summary having less speech recognition errors and low redundancy using candidate words indicated by the internal nodes included in the path selected b

Abstract: A word alignment device including a problem generation unit that receives a first language sentence and a second language sentence as inputs and generates a cross language span prediction problem between the first language sentence and the second language sentence, and a span prediction unit that predicts a span that is an answer to the span prediction problem by using a cross language span prediction model created using correct answer data including a cross language span prediction problem and an answer thereto.

Abstract: The present disclosure relates to an information processing apparatus, an information processing method, and a program capable of supporting work of giving a classification to a group of sentences. A presentation unit presents a sentence included in a cluster of interest among clusters generated by clustering a sentence set in a sentence selection region, and a reception unit receives selection of the sentence constituting a group of sentences from the sentences presented in the sentence selection region. The present disclosure can be applied to, for example, an FAQ construction/search support tool.

Abstract: A class-labeled span sequence identification apparatus includes a span generation unit that generates all spans generable from a unit sequence input, a calculation unit that calculates a probability that each of the spans belongs to an individual class of a plurality of predefined classes, and an identification unit that identifies, from among span sequences generable in accordance with the spans, a class-labeled span sequence having a maximum product of a plurality of the probabilities or a maximum sum of scores according to the plurality of the probabilities, and thereby, improves accuracy of a class segmentation position in the unit sequence.

Abstract: An information processing apparatus (1S) includes: a creation unit (232) configured to create support information for supporting at least one of construction and maintenance of a database on the basis of first information including a representative question and an answer sentence associated with the representative question and stored in the database stored in a storage unit and second information indicating a history of reception with respect to a user; and a processing unit (233) configured to execute processing of performing at least one of construction and maintenance of the database on the basis of input information input to the support information created by the creation unit (232).

Abstract: A toilet device includes a toilet seat disposed on an upper side of a toilet; an image sensor provided on a back surface of the toilet seat and configured to image an inner space of a toilet bowl at time of excretion; an open-close acquiring device configured to acquire open-close information of the toilet seat; and an imaging controller configured to determine whether or not to image by the image sensor according to the open-close information of the toilet seat acquired by the open-close acquiring device.

Abstract: Provided is a hot and cold water mixer capable of performing stable temperature control, including a cold water supply pipe, a hot water supply pipe, a mixing pipe, flow rate adjustment valves, temperature sensors, flow rate sensors, a setting unit, and a control unit. When the control unit determines that either one of the flow rate adjustment valves cannot increase the flow rate, and also determines, by comparing the target flow rate for the one flow rate adjustment valve with the flow rate of water flowing through the one flow rate adjustment valve, that the target flow rate for the one flow rate adjustment valve is higher, the control unit calculates and updates the target flow rate for the other flow rate adjustment valve and controls the other flow rate adjustment valve based on the updated target flow rate.

Abstract: The present disclosure relates to a method of evaluating accuracy of a summary of a document. The method includes receiving a plurality of reference summaries of a document and a system summary of the document. The system summary is generated by a machine. The method further includes extracting, for each reference summary, a tuple that is a pair of words composed of a modified word and a dependent word having a dependency relation to the modified word and a label representing the dependency relation. The method further includes replacing, for each of the extracted tuples, each of the modified word of the tuple's word pair and the dependent word with a class predetermined for the words. The method further generates a score of the system summary based on the class and a set of tuples of the system summary.

Abstract: An information learning apparatus includes a memory and a processor configured to perform encoding first data in training data in which the first data related to a first series and second data which is correct data for the first data in a second series are associated with each other; decoding data generated in the encoding to generate third data related to the second series; fourth data related to the first series for data generated in the decoding; and learning, based on an error between the second data and the third data and an error between the first data and the fourth data, parameters used by the encoding, the decoding, and the generating, wherein the generating and the encoding share parameters.

Abstract: A translation apparatus includes: a preprocessing unit that takes an input sentence in a source language and outputs a token string in which the input sentence has been segmented in tokens, the tokens being a predetermined unit of processing; an output sequence prediction unit that inputs the token string output by the preprocessing unit to a trained translation model and predicts a word translation probability of a translation candidate for each token of the token string from the trained translation model; a word set prediction unit that checks each token of the token string output by the preprocessing unit against entry words of a bilingual dictionary, and upon detecting an entry word that agrees with the token in the bilingual dictionary, generates a target-language word set from a set of tokens constituting a translation phrase corresponding to the detected entry word; and an output sequence determination unit that computes a reward which is based on whether a translation candidate for each token of the i

Abstract: To make it possible to efficiently learn word embedding vectors of respective words contained in two corpora. A basis vector correspondence determination unit 22 determines correspondence between basis vectors obtained from word embedding vectors of respective words generated from a corpus A and basis vectors obtained from word embedding vectors of respective words generated from a corpus B. Based on the determined correspondence, a word embedding vector integration unit 24 changes the word embedding vectors of the respective words contained in the corpus B so as to rearrange elements of the word embedding vectors of the respective words contained in the corpus B.

Abstract: The present invention relates to a method of accurately evaluating a system summary of a document based on one or more predefined units. The method includes dividing the document and the system summary of the document into the one or more predefined units, sentences and phrases, for example. For each of reference summaries for the document, the method generates an oracle. The oracle is a partial set of units that meet a length limitation and maximize a score generated by an evaluation function for a partial set of the units of the document based on the reference summary. The method further includes determines, based on the generated oracle, scores of the respective units included in the set of the oracle. The method further includes determining the score of the system summary based on the score of the system summary unit.

Abstract: A language identification unit (101) identifies types of languages of each of multiple input sentences, which are described in natural language, described in different languages, a multilingual language analysis unit (102) analyzes the syntax structure of each of the input languages in accordance with the types of the languages, a multilingual semantic analysis unit (103) analyzes the semantic structure of each of the input sentences in accordance with the types of the languages, and a multilingual semantic representation comparison unit (104) compares the input sentences to each other based on the analysis results of the semantic structure, calculates a degree of similarity between the input sentences, and appropriately compares the input sentences of different languages to each other based on the degree of similarity calculated by capturing semantic contents of the representation of the input sentences which are text data.

Abstract: An information processing apparatus includes a memory and a processor configured to perform generating subword units of a plurality of layers for each of processing units constituting an input sequence and generating an embedded vector based on the subword units of the plurality of layers for each of the processing units; and executing a process based on a learned parameter, with the embedded vector generated for each of the processing units as an input.

Abstract: An information learning apparatus includes a memory and a processor configured to perform generating, for each of processing units constituting an input sequence included in training data, a third embedded vector based on a first embedded vector for the processing unit and a second embedded vector corresponding to an unknown word; executing a process based on a learning target parameter, with the third embedded vector generated for each of the processing units as an input; and learning, for a processing result by the executing, the parameter based on an error of an output corresponding to the input sequence in the training data.

Abstract: A speech recognition unit (12) converts an input utterance sequence into a confusion network sequence constituted by a k-best of candidate words of speech recognition results; a lattice generating unit (14) generates a lattice sequence having the candidate words as internal nodes and a combination of k words among the candidate words for an identical speech as an external node, in which edges are extended between internal nodes other than internal nodes included in an identical external node, from the confusion network sequence; an integer programming problem generating unit (16) generates an integer programming problem for selecting a path that maximizes an objective function including at least a coverage score of an important word, of paths following the internal nodes with the edges extended, in the lattice sequence; and the summary generating unit generates a high-quality summary having less speech recognition errors and low redundancy using candidate words indicated by the internal nodes included in the

Abstract: Included are input means for inputting first data that is data relating to a plurality of letters included in a text string that is a generation target, and generating means for generating second data that is data relating to the text string that satisfies predetermined constraint conditions including at least a condition relating to plausibility of the sequence of letters, on the basis of the first data.

Abstract: An object is to make it possible to find a high-quality solution at a high speed even for a nonconvex sparse optimization problem of bad conditions. A computation unit 130 computes, with respect to each element that is not included in a set of nonzero elements that is previously obtained or an initial value of the set of nonzero elements, an optimum value of an objective function in a case in which elements that are allowed to be nonzero elements are only elements included in a set that is obtained by adding the element not included in the set of nonzero elements to the set of nonzero elements.