Toward Machine Interpreting: Lessons from Human Interpreting Studies

TL;DR

This paper reviews human interpreting studies to identify principles that can be adopted in speech translation systems, aiming to make them more adaptable and human-like in real-world scenarios.

Contribution

It bridges human interpreting research with machine translation, highlighting how recent modeling techniques can incorporate interpreting principles for improved system behavior.

Findings

Identifies key human interpreting principles relevant to machine translation.

Suggests recent modeling techniques can implement interpreting-like adaptability.

Highlights potential for closing the usability gap in speech translation systems.

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.