A session message generation method, apparatus and device and storage medium. The method includes: acquiring historical session messages between a session robot and a target user in the current man-machine session process and a session reference information set related to a session topic when it is detected that there is a trigger event in the current man-machine session process; determining a target session role of the session robot at the time of performing a message input operation according to the historical session messages and the session reference information set; and generating a target session message corresponding to the target session role based on the historical session messages and the session reference information set, and outputting the target session message.

Embodiments described herein provide a system and method for extracting factual information. The system transforms a query into a natural language prompt in a format of a query subject and a queried relation. The system encodes, via an embedding layer of a pre-trained language model, the natural language prompt into a first embedding. The system encodes, via the adapter model, the first embedding into a second embedding based on a probability that the second embedding returns the factual information when the second embedding is fed the first attention layer of the pre-trained language model. The system decodes, by the first attention layer of the pre-trained language mode, the second embedding into a response to the query. The system extracts the factual information from the decoded response to the query.

Embodiments described herein provide a system and method for extracting factual information. The system transforms a query into a natural language prompt in a format of a query subject and a queried relation. The system encodes, via an embedding layer of a pre-trained language model, the natural language prompt into a first embedding. The system encodes, via the adapter model, the first embedding into a second embedding based on a probability that the second embedding returns the factual information when the second embedding is fed the first attention layer of the pre-trained language model. The system decodes, by the first attention layer of the pre-trained language mode, the second embedding into a response to the query. The system extracts the factual information from the decoded response to the query.

A text generation apparatus includes a processor and a memory storing program instructions that cause the processor to receive an input sentence including one or more words and generate a sentence: estimate an importance of each word included in the input sentence and encode the input sentence; and take the importance and a result of encoding the input sentence as inputs to generate the sentence based on the input sentence. The processor uses a neural network based on learned parameters. This improves the accuracy of sentence generation.

A text generation apparatus includes a processor and a memory storing program instructions that cause the processor to receive an input sentence including one or more words and generate a sentence: estimate an importance of each word included in the input sentence and encode the input sentence; and take the importance and a result of encoding the input sentence as inputs to generate the sentence based on the input sentence. The processor uses a neural network based on learned parameters. This improves the accuracy of sentence generation.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

In one embodiment, a method includes receiving a user request including an ambiguous mention to create a reminder from a client system associated with a user, disambiguating the mention to identify a first entity referenced in the mention, wherein the first entity is identified based on user profile data associated with the user, determining an activation condition associated with the user request, wherein the activation condition is based on one or more of a time or a location referenced in the user request, wherein the time and/or location are determined based on contextual information associated with the user request, generating the reminder based on the first entity and the activation condition, and sending instructions to the client system for presenting the reminder when the activation condition is satisfied.

Provided is a non-transitory storage medium that stores a computer readable program for a computer of a medical information processing device configured to output and/or input medical information, the program causing the computer to perform acquiring locale information in environment information of the medical information processing device and presuming a character encoding of the medical information based on the locale information acquired in the acquiring.

Provided is a non-transitory storage medium that stores a computer readable program for a computer of a medical information processing device configured to output and/or input medical information, the program causing the computer to perform acquiring locale information in environment information of the medical information processing device and presuming a character encoding of the medical information based on the locale information acquired in the acquiring.

A character restoration method and apparatus, a storage medium, and an electronic device are provided. The character restoration method includes: a character identifier of a character in a text region is determined, where the character identifier is used for uniquely identifying the character; and encoding is performed at least according to the character identifier, and encoded data is sent to a receiving end, where the encoded data is used for the receiving end to decode the encoded data and restore the character according to the character identifier obtained after decoding, that is, encoding is performed merely according to a small amount of information, and then the information is obtained by decoding, so as to restore the character.