Abstract: A task execution method for a large model, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence technology, particularly to fields of deep learning technology and large model technology.

Abstract: A data processing method, and a data processing model and a training method therefor are provided, and relate to the field of artificial intelligence, and specifically, to natural language processing, deep learning technologies, and large model technologies. An implementation solution includes: determining input data, where the input data includes a plurality of tokens; determining a correlation between each of the plurality of tokens and each of a plurality of expert networks based on a gating matrix, where the plurality of expert networks are used to reinforce the plurality of tokens; allocating the plurality of tokens to the plurality of expert networks in a uniform manner based on the correlation and a preset capacity of each expert network, to reinforce the plurality of tokens; and determining a data processing result based on the plurality of reinforced tokens.

Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.

Abstract: In a method for processing a document image, a document image to be processed is acquired. Text nodes of multiple granularities, visual nodes of multiple granularities, respective node information of the text nodes, and respective node information of the visual nodes in the document image are obtained. A multi-granularity and multi-modality document graph is construct based on the text nodes of multiple granularities, the visual nodes of multiple granularities, the respective node information of the text nodes and the respective node information of the visual nodes. Multi-granularity semantic feature information of the document image is determined based on the multi-granularity and multi-modality document graph, the respective node information of the text nodes and the respective node information of the visual nodes.

Abstract: A neural network training method and a document image understanding method is provided. The neural network training method includes: acquiring text comprehensive features of a plurality of first texts in an original image; replacing at least one original region in the original image to obtain a sample image including a plurality of first regions and a ground truth label for indicating whether each first region is a replaced region; acquiring image comprehensive features of the plurality of first regions; inputting the text comprehensive features of the plurality of first texts and the image comprehensive features of the plurality of first regions into a neural network model together to obtain text representation features of the plurality of first texts; determining a predicted label based on the text representation features of the plurality of first texts; and training the neural network model based on the ground truth label and the predicted label.

Abstract: The present disclosure provides a method and apparatus for training a document information extraction model and method and apparatus for extracting document information, and relates to the field of artificial intelligence, and more particularly to the field of natural language processing. A specific implementation solution is: acquiring training data labeled with an answer corresponding to a preset question and a document information extraction model, the training data includes layout document training data and streaming document training data; extracting at least one feature from the training data; fusing at least one feature to obtain a fused feature; inputting the preset question, the fused feature and the training data into the document information extraction model to obtain a predicted result; and adjusting network parameters of the document information extraction model based on the predicted result and the answer.

Abstract: A method and an apparatus for processing a table are provided. The method includes: obtaining text information of cells in the table; obtaining structure information of the cells in the table; and inputting a query word, the text information, and the structure information of the table into a table information extraction model to obtain an answer output from the table information extraction model, wherein the output answer corresponds to the query word in the table.

Abstract: A method for extracting text information includes: acquiring a text to be extracted and a target field name; extracting candidate text information matching the target field name from the text to be extracted based on the text to be extracted and the target field name; and acquiring target text information matching fusion semantics of the text to be extracted, the target field name and the candidate text information by filtering the candidate text information based on the fusion semantics. Therefore, when the candidate text information matching the target field name is extracted from the text to be extracted, the candidate text information is filtered based on the fusion semantics of the text to be extracted, the target field name and the candidate text information, which improves the accuracy of extracting text information.

Abstract: A method for extracting text information includes: acquiring a text to be extracted and a target field name; extracting candidate text information matching the target field name from the text to be extracted based on the text to be extracted and the target field name; and acquiring target text information matching fusion semantics of the text to be extracted, the target field name and the candidate text information by filtering the candidate text information based on the fusion semantics. Therefore, when the candidate text information matching the target field name is extracted from the text to be extracted, the candidate text information is filtered based on the fusion semantics of the text to be extracted, the target field name and the candidate text information, which improves the accuracy of extracting text information.

Abstract: The present disclosure provides a text processing method and apparatus, an electronic device and a storage medium, and relates to the field of artificial intelligence technologies such as deep learning and natural language processing. The method may include: configuring, for a to-be-processed text, attention patterns corresponding to heads in a Transformer model using a multi-head-attention mechanism respectively, wherein at least one head corresponds to a different attention pattern from the other N?1 heads, and N denotes a number of heads and is a positive integer greater than 1; and processing the text by using the Transformer model. Model performance and a corresponding text processing effect can be improved by using the solutions according to the present disclosure.

Abstract: The present disclosure provides a method and apparatus for generating a node representation, an electronic device and a readable storage medium, and relates to the field of deep learning technologies. The method for generating a node representation includes: acquiring a heterogeneous graph to be processed; performing a sampling operation in the heterogeneous graph to be processed according to a first meta path, so as to obtain at least one first walk path; obtaining an initial node representation of each node in the heterogeneous graph to be processed according to the at least one first walk path; and generating the final node representation of each node according to the initial node representation of each node and initial node representations of neighbor nodes of each node. With the present disclosure, accuracy of the generated node representation may be improved.

Abstract: The present disclosure provides a method, apparatus, electronic device and storage medium for processing a semantic representation model, and relates to the field of artificial intelligence technologies. A specific implementation solution is: collecting a training corpus set including a plurality of training corpuses; training the semantic representation model using the training corpus set based on at least one of lexicon, grammar and semantics. In the present disclosure, by building the unsupervised or weakly-supervised training task at three different levels, namely, lexicon, grammar and semantics, the semantic representation model is enabled to learn knowledge at levels of lexicon, grammar and semantics from massive data, enhance the capability of universal semantic representation and improve the processing effect of the NLP task.

Abstract: The present disclosure provides a training method and apparatus for a document processing model, a device, a storage medium and a program, which relate to the field of artificial intelligence, and in particular, to technologies such as deep learning, natural language processing and text recognition. The specific implementation is: acquiring a first sample document; determining element features of a plurality of document elements in the first sample document and positions corresponding to M position types of each document element according to the first sample document; where the document element corresponds to a character or a document area in the first sample document; and performing training on a basic model according to the element features of the plurality of document elements and the positions corresponding to the M position types of each document element to obtain the document processing model.

Abstract: The present application provides a method of training a natural language processing model, which relates to a field of artificial intelligence, and in particular to a field of natural language processing. A specific implementation scheme includes: performing a semantic learning for multi-tasks on an input text, so as to obtain a semantic feature for the multi-tasks, wherein the multi-tasks include a plurality of branch tasks; performing a feature learning for each branch task based on the semantic feature, so as to obtain a first output result for each branch task; calculating a loss for each branch task according to the first output result for the branch task; and adjusting a parameter of the natural language processing model according to the loss for each branch task. The present application further provides a method of processing a natural language, an electronic device, and a storage medium.

Abstract: A pre-training method of a neural network model, an electronic device, and a medium. The pre-training data is inputted to the initial neural network model, and the initial neural network model is pre-trained in the first training mode, in the first training mode, the plurality of hidden layers share one hidden layer parameter, and the loss value of the initial neural network model is obtained, if the loss value of the initial neural network model is less than a preset threshold, the initial neural network model continues to be pre-trained in the second training mode, in the second training mode, each of the plurality of hidden layers has its own hidden layer parameter.

Abstract: Embodiments of the present disclosure provide a method for generating a model for representing heterogeneous graph node. A specific implementation includes: acquiring a training data set, wherein the training data set includes node walk path information obtained by sampling a heterogeneous graph according to different meta paths; and training, based on a gradient descent algorithm, an initial heterogeneous graph node representation model with the training data set as an input of the initial heterogeneous graph node representation model, to obtain a heterogeneous graph node representation model.

Abstract: A method for pre-training a graph neural network, an electronic device and a readable storage medium, which relate to the technical field of deep learning are proposed. An embodiment for pre-training a graph neural network includes: acquiring an original sample to be used for training; expanding the original sample to obtain a positive sample and a negative sample corresponding to the \original sample; constructing a sample set Corresponding to the original sample by using the original sample and the positive sample, the negative sample, and a weak sample corresponding to the original sample; and pre-training the graph neural network by taking the original sample and one of other samples in the sample set as input of the graph neural network respectively, until the graph neural network converges. The technical solution may implement pre-training of a graph neural network at a graph level.

Abstract: The disclosure discloses a method for distilling a model, an electronic device, and a storage medium, and relates to the field of deep learning technologies. A teacher model and a student model are obtained. The second intermediate fully connected layer is transformed into an enlarged fully connected layer and a reduced fully connected layer based on a first data processing capacity of a first intermediate fully connected layer of the teacher model and a second data processing capacity of a second intermediate fully connected layer of the student model. The second intermediate fully connected layer is replaced with the enlarged fully connected layer and the reduced fully connected layer to generate a training student model. The training student model is distilled based on the teacher model.

Abstract: Embodiments of the disclosure disclose a method and apparatus for recommending multimedia resources, an electronic device and a storage medium. The present disclosure relates to a nature language processing technology. The technology solution can be implemented by generating a relation graph based on first multimedia resources browsed by a plurality of user objects, taking two nodes between which a number of nodes in a path of the relation graph is less than a number threshold as a training sample, training a graph model to obtain a representative vector of each node in the training samples and recommending multimedia resources based on vector similarities between the representative vectors of respective nodes of the plurality of user objects and/or the representative vectors of respective nodes of the first multimedia resources.

Abstract: The disclosure provides a method for training a semantic analysis model, an electronic device and a storage medium. The method includes: obtaining a plurality of training data, in which each of the plurality of training data comprises a search word, information on at least one text obtained by searching the search word, and at least one associated word corresponding to the at least one text; constructing a graph model based on the training data, and determining target training data from the plurality of training data by using the graph model, the target training data comprising search word samples, information samples and associated word samples; and training a semantic analysis model based on the search word samples, the information samples, and the associated word samples.