Abstract: The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge.

Abstract: A controller for controlling an electric motor module equipped with incremental encoder and operation method thereof are provided. The controller includes a quadruple frequency circuit, a driver circuit, a non-volatile memory (NVM) and a multi-phase control circuit. The multi-phase control circuit can perform multi-phase control with aid of the NVM, for example: reading an offset counter value from the NVM; executing an initial angle estimation procedure, generating an initial counter value according to an estimated initial angle and the offset counter value, and starting utilizing the driver circuit to directly control the electric motor to start with the estimated initial angle and utilizing a counter to perform counting operations; calculating a counter value error and clear the current counter value to be zero; and performing compensation corresponding to a predetermined compensation times count according to the counter value error, respectively, to control the rotor to reach a target angle.

Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence.

Abstract: The present disclosure provides a multi-lingual model training method, apparatus, electronic device and readable storage medium and relates to the technical field of deep learning and natural language processing. A technical solution of the present disclosure when training the multi-lingual model is: obtaining training corpuses comprising a plurality of bilingual corpuses and a plurality of monolingual corpuses; training a multi-lingual model with a first training task by using the plurality of bilingual corpuses; training the multi-lingual model with a second training task by using the plurality of monolingual corpuses; and completing the training of the multi-lingual model in a case of determining that loss functions of the first training task and second training task converge.

Abstract: Technical solutions relate to the natural language processing field based on artificial intelligence. According to an embodiment, a multilingual semantic representation model is trained using a plurality of training language materials represented in a plurality of languages respectively, such that the multilingual semantic representation model learns the semantic representation capability of each language; a corresponding mixed-language language material is generated for each of the plurality of training language materials, and the mixed-language language material includes language materials in at least two languages; and the multilingual semantic representation model is trained using each mixed-language language material and the corresponding training language material, such that the multilingual semantic representation model learns semantic alignment information of different languages.

Abstract: The present application discloses a method and apparatus for training a natural language processing model, a device and a storage medium, which relates to the natural language processing field based on artificial intelligence. An implementation includes: constructing training language material pairs of a coreference resolution task based on a preset language material set, wherein each training language material pair includes a positive sample and a negative sample; training the natural language processing model with the training language material pair to enable the natural language processing model to learn the capability of recognizing corresponding positive samples and negative samples; and training the natural language processing model with the positive samples of the training language material pairs to enable the natural language processing model to learn the capability of the coreference resolution task.

Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence.