WiMi Announced Deep Learning-Based Machine Reading Comprehension Models

Rhea-AI Summary

WiMi Hologram Cloud Inc. announces the application of deep learning in machine reading comprehension to improve machine performance and accuracy. The model uses deep neural network models to transform text into a vector representation, allowing the model to extract information and generate accurate answers. WiMi's machine reading comprehension model includes input representation, contextual understanding, question comprehension, and answer generation. The company plans to further develop the model by integrating multi-modal data and focusing on migration learning and adaptive learning.

BEIJING, Nov. 3, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today 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.

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.

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.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

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SOURCE WiMi Hologram Cloud Inc.

FAQ

What is WiMi Hologram Cloud Inc.?

WiMi Hologram Cloud Inc. is a leading global Hologram Augmented Reality (AR) Technology provider.

What is the application of deep learning in machine reading comprehension?

Deep learning is used to transform text into a vector representation, allowing the model to extract information and generate accurate answers.

What are the key components of WiMi's machine reading comprehension model?

The key components include input representation, contextual understanding, question comprehension, and answer generation.

What are the future development directions of machine reading comprehension models?

The future development directions include multi-modal integration, cross-language and cross-domain applications, and migration learning and adaptive learning.

What is multi-modal integration in machine reading comprehension models?

Multi-modal integration involves handling inputs from multiple modalities such as images, speech, and text to provide more comprehensive understanding and accurate answers.

What is migration learning and adaptive learning in machine reading comprehension models?

Migration learning and adaptive learning focus on improving the generalization ability of models by using existing knowledge and models to quickly learn and adapt to new tasks and domains.