WiMi Announced Deep Learning-Based Machine Reading Comprehension Models

WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that deep learning is applied into a machine reading comprehension model and combined with techniques such as data augmentation and model correction to improve machine readability and comprehensibility of human language and to improve machine performance and accuracy in reading comprehension tasks.

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The application of deep learning in machine reading comprehension mainly refers to the use of deep neural network models to solve machine reading comprehension problems. The basic principle is to realize the ability of automatic reading and comprehension by transforming the text into a vector representation to capture the semantic information of the words and using the attention mechanism and decoding algorithm. This model is capable of extracting information from a large amount of text and generating accurate answers according to the questions. The model usually contains key components such as word embedding, encoding, and decoding.

WiMi’s machine reading comprehension modeling based on deep learning includes input representation, contextual understanding, question comprehension, and answer generation. Input representation refers to the transformation of raw text into a machine-processable form. Through the comprehensive use of input representation methods such as word embedding, character embedding and positional coding, the machine reading comprehension model can better understand the semantic and structural information in the text, thus improving the model’s performance in reading comprehension tasks. Contextual understanding is a very important part of a machine reading comprehension model, which helps the model to understand the contextual information in the text so that it can answer the questions better. In this model, a common approach is to realize contextual understanding through the attention mechanism. Through contextual understanding, the reading comprehension model can better understand the text and improve the accuracy and efficiency of question answering. In machine reading comprehension tasks, question comprehension refers to the transformation of a given question into a form that can be understood and processed by a machine. The goal of question comprehension is to extract the key information from the question and match it to the context in order to find the correct answer. Through the process of question comprehension, we can transform a given question into a form that can be understood and processed by a machine and find the correct answer. This provides the basis for success in machine reading comprehension tasks. Answer generation is an important step in machine reading comprehension modeling where the goal is to generate an accurate and coherent answer based on the model’s understanding of the question and the text.

With the continuous development of deep learning technology, machine reading comprehension models are also evolving. In the future, the development direction of machine reading comprehension models mainly includes multi-modal integration, cross-language and cross-domain applications, and migration learning and adaptive learning. With the wide application of multi-modal data, future machine reading comprehension models will be able to handle multi-modal inputs such as combinations of images, speech and text. By integrating information from multiple modalities, the model can understand the text more comprehensively and provide more accurate answers.’

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To solve the problems of data scarcity and domain adaptation, in the future, WiMi’s research on machine reading comprehension models will pay more attention to migration learning and adaptive learning, and improve the generalization ability of the models by using existing knowledge and models to learn and migrate quickly in new tasks and domains. WiMi will also continue to conduct in-depth research in the field of machine reading comprehension models, to make the machine reading comprehension models more powerful and intelligent to better understand and apply textual information to provide more help and support to human beings.

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