Yangzhou Polytechnic College, Jiangsu, Yangzhou 225009, China.
J Environ Public Health. 2022 Sep 30;2022:4702003. doi: 10.1155/2022/4702003. eCollection 2022.
Artificial intelligence now plays a significant role in both daily life and scientific research because of the rapid advancement of this technology in recent years. Making full use of the phrases in the translation phrase table for translation is challenging since the phrase matching is too accurate when the translation machine decodes. Fully automatic machine translation struggles to meet the expectations of its users since there are more or less translation faults brought on by data bottlenecks. Therefore, we require collaborative assisted translation technology for human-computer interaction. This work strengthens the research on collaborative translation techniques and ways for monitoring the human-computer interaction environment in order to further improve translation quality. This essay investigates and discusses human-computer translation techniques as well as related ideas in collaborative translation and human-computer interaction. The translation similarity model is incorporated into the translation system model together with an overall qualitative knowledge and logical reasoning capability of human-computer interaction to offer fresh strategies and methods for collaborative translation between humans and computers. According to the experimental findings, the accuracy rate of the collaborative translation system for human-computer interaction based on artificial intelligence technology can achieve 98.2% and 95.6%. The quality of the translation is enhanced after human-computer interaction, and the editing gap between the incorrect and auxiliary translations is narrowed, demonstrating the efficiency of the system and demonstrating its viability. In order to enhance the accuracy of system translation and the effectiveness of system operation, it is important to investigate the collaborative translation mode of human-computer interaction based on artificial intelligence technology.
由于近年来人工智能技术的快速发展,它在日常生活和科学研究中都扮演着重要的角色。由于翻译机器在解码时短语匹配过于准确,充分利用翻译短语表中的短语进行翻译具有挑战性。由于数据瓶颈导致或多或少存在翻译错误,完全自动的机器翻译难以满足用户的期望。因此,我们需要人机交互的协作辅助翻译技术。这项工作加强了对协作翻译技术以及人机交互环境监控的研究,以便进一步提高翻译质量。本文研究和讨论了人机翻译技术以及协作翻译和人机交互中的相关思想。将翻译相似度模型与人机交互的整体定性知识和逻辑推理能力结合到翻译系统模型中,为人机协作翻译提供了新的策略和方法。根据实验结果,基于人工智能技术的人机交互协作翻译系统的准确率可以达到 98.2%和 95.6%。人机交互后提高了翻译质量,缩小了错误翻译和辅助翻译之间的编辑差距,展示了系统的效率和可行性。为了提高系统翻译的准确性和系统运行的有效性,研究基于人工智能技术的人机交互协作翻译模式非常重要。