Abstract: The present invention relates to application of gossypol and its optical isomers to preparation of an anti-Coronavirus drug. Specifically disclosed is application of pharmaceutically acceptable salts, solvates, isotopes, stereoisomers, stereoisomer mixtures, and tautomers of the gossypol and its optical isomers to preparation of a drug for preventing and/or treating a disease caused by a Coronavirus. The Coronavirus is MERS-CoV, SARS-CoV, or SARS-CoV-2. It is found in the present invention for the first time that the gossypol and its optical isomers can inhibit the activity of Coronavirus 3CL proteases to achieve an anti-Coronavirus effect. The half maximal inhibitory concentrations of gossypol and (?)-gossypol to SARS-CoV proteases and SARS-CoV-2 3CL proteases are all lower than 10 ?M. The anti-Coronavirus 3CL protease effect is good. Therefore, the gossypol and its optical isomers have the potential for use in the preparation of a drug for preventing and/or treating novel Coronavirus infections.

Abstract: Techniques for presenting an alternative input representation to a user for testing and collecting processing data are described. A system may determine that a received spoken input triggers an alternative input representation for presenting. The system may output data corresponding to the alternative input representation in response to the received spoken input, and the system may receive user feedback from the user. The system may store the user feedback and processing data corresponding to processing of the alternative input representation, which may be later used to update an alternative input component configured to determine alternative input representations for spoken inputs.

Abstract: A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.

Abstract: A system is provided for a self-learning policy engine that can be used by various spoken language understanding (SLU) processing components. The system also provides for sharing contextual information from processing performed by an upstream SLU component to a downstream SLU component to facilitate decision making by the downstream SLU component. The system also provides for a SLU component to select from a variety of actions to take. A SLU component may implement an instance of the self-learning policy that is specifically configured for the particular SLU component.

Abstract: According to some aspects, a method of training a transformation component using a trained acoustic model comprising first parameters having respective first values established during training of the acoustic model using first training data is provided. The method comprises using at least one computer processor to perform coupling the transformation component to a portion of the acoustic model, the transformation component comprising second parameters, and training the transformation component by determining, for the second parameters, respective second values using second training data input to the transformation component and processed by the acoustic model, wherein the acoustic model retains the first parameters having the respective first values throughout training of the transformation component.

Abstract: According to some aspects, a method of training a transformation component using a trained acoustic model comprising first parameters having respective first values established during training of the acoustic model using first training data is provided. The method comprises using at least one computer processor to perform coupling the transformation component to a portion of the acoustic model, the transformation component comprising second parameters, and training the transformation component by determining, for the second parameters, respective second values using second training data input to the transformation component and processed by the acoustic model, wherein the acoustic model retains the first parameters having the respective first values throughout training of the transformation component.