Abstract: There is provided a method that includes obtaining (a) a dense representation of external features, (b) a dense representation of text, and (c) a mask that associates the external features to tokens of the text, and employing a cross-attention process that utilizes the mask to perform an information fusion of the dense representation of the external features and the tokens of the text, thus yielding a joint representation of the external features and the tokens of the text. There is also provided a system that executes the method, and a storage device that includes instructions for controlling a processor to perform the method.

Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.

Abstract: A system and method incorporate prior knowledge into the optimization and regularization of a classification and regression model. The optimization may be a regularization process and the prior knowledge may be incorporated through adjustment of a cost function. A method of at least one processor developing a classification and regression model may be provided. The method may be implemented by at least one processor that implements classification and regression model functionality, including receiving training data and adjusting the model according to the training data; testing the classification and regression model; and employing prior knowledge during an optimization of the classification and regression model. The regularizing can include adjusting feature weights according to prior knowledge. In various embodiments, such systems and methods can be used in the processing of language inputs, e.g., speech and/or text inputs, to achieve greater interpretation accuracy.