Image-Text Multimodal Translation Based on AIGC Human-Machine Interaction

What is it about?

This research looks at how machine translation works by studying the development of artificial intelligence (AI) in language processing. It focuses on how words are turned into numbers using a process called vectorization. Two neural network models, CBOW and Skip-gram, are used to analyze this process. The study also examines the Transformer model, which uses a self-attention mechanism, and highlights the importance of Layer Normalization for making training stable and fast. The research discusses ChatGPT, a cutting-edge AI model that can hold conversations. ChatGPT helps translators understand and generate language. It talks about how generative AI is used in translation, where humans and machines work together to make the most of human intelligence while using AI's abilities. Additionally, the research looks at DALL·E2, an AI that can create images from text. Combining these images with translated text is important for creating works that mix different types of communication, like text and images. This combination helps to effectively express emotions through the interaction between text and images.

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Photo by Possessed Photography on Unsplash

Photo by Possessed Photography on Unsplash

Why is it important?

The operational mode of machine translation has significantly evolved with advancements in AI, particularly in language processing and vocabulary vectorization. The emergence of ChatGPT has further enhanced translation quality by aiding language comprehension and generation. DALL·E2 introduces new possibilities for creative expression by integrating text and image modalities in translation, enhancing acceptance of translated works.

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