Human-Computer Interaction Environment Monitoring and Collaborative Translation Mode Exploration Using Artificial...

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Rapid advancements in artificial intelligence (AI) have increased its role in daily life and scientific research.
• Fully automatic machine translation faces challenges due to data bottlenecks and overly precise phrase matching, leading to translation faults.
• Existing machine translation systems struggle to meet user expectations, necessitating collaborative assisted translation.

Purpose of the Study:

• To strengthen research on collaborative translation techniques and human-computer interaction (HCI) monitoring for improved translation quality.
• To investigate human-computer translation techniques and related concepts in collaborative translation and HCI.
• To introduce novel strategies for human-computer collaborative translation by integrating a translation similarity model with HCI's qualitative knowledge and logical reasoning.

Main Methods:

• Development of a collaborative translation system integrating artificial intelligence (AI) and human-computer interaction (HCI).
• Incorporation of a translation similarity model into the system architecture.
• Integration of qualitative knowledge and logical reasoning capabilities from HCI into the AI translation system.

Main Results:

• The AI-based collaborative translation system achieved accuracy rates of 98.2% and 95.6%.
• Human-computer interaction significantly enhanced translation quality.
• The system narrowed the editing gap between incorrect and auxiliary translations, demonstrating efficiency and viability.

Conclusions:

• The developed human-computer interaction collaborative translation mode based on AI technology is effective.
• The system enhances translation accuracy and operational efficiency.
• Further investigation into this collaborative mode is crucial for advancing AI-powered translation systems.