Abstract: A learning device includes a memory; and a processor configured to execute answer generation means for taking data including text, and a question text related to the data as inputs; creating, by using a model parameter of a neural network, a token sequence that takes visual information in the data into consideration, and generating an answer text to the question text, based on the created token sequence; and learning means for learning the model parameter by using the answer text and a correct answer text to the question text.

Abstract: A summary generation device includes: an image processing unit that receives an input of an image related to a moving image, and extracts at least a text from the image; a sound processing unit that receives an input of sound in the moving image, and extracts at least a text from the sound; and a summary generation unit that generates a summary text of the moving image from information extracted from the image and information extracted from the sound, using a trained summary model.

Abstract: A learning apparatus includes a memory and at least one processor connected to the memory, wherein the processor configured to: convert input text data into a feature amount sequence based on a language model; and update parameters of the language model based on the text data, the feature amount sequence, and a word vector learned in advance.

Abstract: A language processing device includes a hardware processor configured to extract a feature amount from text data based on model parameters of a neural network; output an answer start point score, an answer end point score, and an answer possibility score using the feature amount as an input, based on model parameters of the neural network; extract predetermined n answer suitability scores based on the answer start point score and the answer end point score; obtain n adjusted answer suitability scores from the n answer suitability scores and obtain an adjusted answer possibility score from the answer possibility score, based on model parameters of the neural network; and lean the model parameters based on the n adjusted answer suitability scores, the adjusted answer possibility score, a correct answer section, and a correct answer possibility.

Abstract: A language processing device includes circuitry configured to generate an error sentence corresponding to an original sentence based on pronunciation corresponding to text data indicating the original sentence; use a language model based on a neural network model to generate a prediction sentence from the error sentence based on a language model parameter of the language model; and update the language model parameter based on a difference between the original sentence and the prediction sentence.

Abstract: A learning apparatus includes a memory and at least one processor connected to the memory, wherein the processor configured to: convert input text data into a feature amount sequence based on a language model; and update parameters of the language model based on the text data, the feature amount sequence, and a word vector learned in advance.

Abstract: A learning device includes a memory; and a processor configured to execute answer generation means for taking data including text, and a question text related to the data as inputs; creating, by using a model parameter of a neural network, a token sequence that takes visual information in the data into consideration, and generating an answer text to the question text, based on the created token sequence; and learning means for learning the model parameter by using the answer text and a correct answer text to the question text.