Abstract: A large model-based method of generating a text, a method of training a text generation model, a device, and a medium are provided, which relate to a field of artificial intelligence technology, specifically to fields of deep learning, natural language processing and large model technologies. The large model-based method of generating a text includes: acquiring a memory state for a text to be processed, where the memory state is generated based on a previous text of the text to be processed; determining an embedding feature of the text to be processed as an initial hidden state, and processing the memory state and the initial hidden state by using a first attention mechanism to obtain an updated hidden state; and generating a subsequent text for the text to be processed based on the updated hidden state.

Abstract: A method for generating a dialogue state includes: acquiring a target dialogue state of a previous round of dialogue and dialogue information of a current round of dialogue; generating an initial dialogue state of the current round of dialogue according to the target dialogue state of the previous round of dialogue and the dialogue information of the current round of dialogue; and generating a target dialogue state of the current round of dialogue according to the initial dialogue state of the current round of dialogue and the dialogue information of the current round of dialogue.

Abstract: A method for processing a dialogue includes: obtaining a dialogue text of the dialogue, in which the dialogue text includes a current question text, or the dialogue text includes the current question text and a historical dialogue text; extracting a current query text from the dialogue text; obtaining a knowledge query result for the current query text by querying a knowledge database based on the current query text; and determining a response text for the current question text based on the knowledge query result and the dialogue text.

Abstract: A method for answer questioning based on a table includes the following. A question text to be processed and an information table for question answering are determined, and the information table includes: at least one attribute name. A character vector sequence, a position vector sequence and a type vector sequence are determined based on the question text and the at least one attribute name. An attribute name segment and an attribute value segment in the question text are determined based on the character vector sequence, the position vector sequence and the type vector sequence. An answer corresponding to the question text is determined based on the attribute name segment, the attribute value segment and the information table.

Abstract: A method for generating dialog data is provided. An implementation is: obtaining a target dialog data template, where the target dialog data template includes one or more target single-round dialog data templates, each target single-round dialog data template includes one or more keyword slots and related information about each keyword slot, and the related information about each keyword slot includes location information and attribute information; for each keyword slot, determining, from a keyword data set at least based on the attribute information of the keyword slot, one or more target keywords that match the keyword slot; and for each target single-round dialog data template, filling the target single-round dialog data template with the one or more target keywords based on the location information of the one or more keyword slots, to obtain target dialog data.

Abstract: A method for generating a dialogue state includes: acquiring a target dialogue state of a previous round of dialogue and dialogue information of a current round of dialogue; generating an initial dialogue state of the current round of dialogue according to the target dialogue state of the previous round of dialogue and the dialogue information of the current round of dialogue; and generating a target dialogue state of the current round of dialogue according to the initial dialogue state of the current round of dialogue and the dialogue information of the current round of dialogue.

Abstract: An intelligent dialogue method and apparatus and medium are provided. The method includes: obtaining a pre-matching result by pre-matching a query to be processed with a table content of a target table; extracting a character segment having a highest matching degree with the attribute value from the query based on the attribute value having the highest matching degree with the query; determining a target attribute value semantically associated with the character segment based on the attribute value of each column attribute; generating a structured query language (SQL) query statement corresponding to the query based on the query, the attribute name of each column attribute, the highest matching level of the attribute name, the highest matching level of the attribute value and the target attribute value; and generating a reply statement based on a result obtained by searching a database based on the SQL query statement.

Abstract: A human-machine interaction method and apparatus based on artificial intelligence. In the method, a user-entered interaction sentence is received, and whether to generate an interaction result corresponding to the interaction sentence is determined; and interaction information to be presented to the user is determined based on a determining result, the interaction information including at least one of following items: the generated interaction result corresponding to the interaction sentence, or a search result corresponding to the interaction sentence in a search engine.

Abstract: Embodiments of the present disclosure disclose an artificial intelligence based method and apparatus for classifying a voice-recognized text. A specific embodiment of the method includes: acquiring a current interactive text of a voice query from a user; analyzing the current interactive text using a lexical analyzer to obtain a current lexical structure; determining whether the current lexical structure matches a template of a category in a classifier; and classifying, if the current lexical structure matches the template of the category in the classifier, the current interactive text corresponding to the current lexical structure into the category belonging to the matched template. The embodiment can fast classify texts, effectively reduce the magnitude of manually annotated texts, and improve the annotation efficiency in intelligent voice interaction services.

Abstract: The disclosure discloses a human-machine interaction method and apparatus based on artificial intelligence. A specific embodiment of the method comprises: receiving a user-entered interaction sentence, and determining whether to generate an interaction result corresponding to the interaction sentence; and determining interaction information to be presented to the user based on a determining result, the interaction information comprising at least one of following items: the generated interaction result corresponding to the interaction sentence, or a search result corresponding to the interaction sentence in a search engine.

Abstract: Embodiments of the present disclosure disclose an artificial intelligence based method and apparatus for classifying a voice-recognized text. A specific embodiment of the method includes: acquiring a current interactive text of a voice query from a user; analyzing the current interactive text using a lexical analyzer to obtain a current lexical structure; determining whether the current lexical structure matches a template of a category in a classifier; and classifying, if the current lexical structure matches the template of the category in the classifier, the current interactive text corresponding to the current lexical structure into the category belonging to the matched template. The embodiment can fast classify texts, effectively reduce the magnitude of manually annotated texts, and improve the annotation efficiency in intelligent voice interaction services.