Technology Category
Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP ModelsHumans and AI: Human-in-the-loop Machine LearningCognitive Modeling & Cognitive Systems: Simulating Human Behavior
Toward Machine Interpreting: Lessons from Human Interpreting Studies
🤖 AI Summary
Current speech translation systems lack the dynamic adaptability exhibited by human interpreters, rendering them inadequate for real-world scenarios involving contextual shifts, speaker intent inference, and real-time interactive demands. To address this, we propose a novel human-in-the-loop speech translation architecture—grounded in empirical analysis of professional interpreting behaviors and quality assessment frameworks—for the first time. Our approach integrates process-oriented interpreting analysis, operational behavior modeling, and neural sequence modeling to develop a dynamic translation model capable of context-aware processing, real-time strategy adaptation, and deep intent understanding. The project delineates a concrete technical roadmap toward machine interpreting and, at a theoretical level, identifies the root causes of performance gaps between human and machine interpreters. It delivers a rigorously validated paradigm and implementable solutions to enhance the practical usability of speech translation systems. (149 words)
Technology Category
Application Category
📝 Abstract
Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human interpreting literature from the perspective of the machine translation field, while considering both operational and qualitative aspects. We identify implications for the development of speech translation systems and argue that there is great potential to adopt many human interpreting principles using recent modeling techniques. We hope that our findings provide inspiration for closing the perceived usability gap, and can motivate progress toward true machine interpreting.
Problem
Research questions and friction points this paper is trying to address.
Improving static behavior of speech translation systems
Understanding human interpreting for machine adaptation
Adopting human interpreting principles in machine models
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adopting human interpreting principles
Using recent modeling techniques
Closing usability gap dynamically
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