Patent ID: 11861307
Assignee: NATIONAL INSTITUTE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. A computer-implemented method comprising:
receiving, by at least one computer processor, an input word vector sequence as an input;
outputting, by at least one computer processor configured to execute a request classification model, a classification code indicating one of a predetermined plurality of request classes to which the input word vector sequence belongs; and
converting, by at least one computer processor configured to execute a request paraphrasing model, a request represented by the input word vector sequence and the classification code of the request, to an output word sequence corresponding to a request having a higher probability of getting a right response from a prescribed dialogue system than the input word vector sequence;
storing, in a training data storage device, a plurality of training data items for training the request paraphrasing model, each of the plurality of training data items including a first request including a word sequence, a classification code indicating one of a plurality of request classes to which the first request belongs, and a second request including a word sequence as a paraphrase of the first request; and
training the request paraphrasing model, for each of the plurality of training data items stored in the training data storage device, using the first request and the classification code as inputs and the second request as teacher data, wherein:
a classification model is implemented by a convolutional neural network, the classification model including multiple sets of layers, each set of layers comprising:
 an input layer including multiple instances of a word vector sequence that are collectively represented as a matrix “T”, wherein a number “M” feature maps are applied to the matrix T;
 a convolutional layer; and
 a pooling layer, wherein an element of each feature map is computed by applying a filter represented by fj (1≤j≤M) to an “N”-gram comprised of consecutive “N” word vectors and “N” is an arbitrary natural number.