# Automatic Estimation of Simultaneous Interpreter Performance

arXiv: https://arxiv.org/abs/1805.04016 · HTML: https://mlsanity.com/paper/1805.04016/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 3 of 3)

- [Who Are We Talking About? Handling Person Names in Speech Translation](https://mlsanity.com/paper/2205.06755/) (2022)
- [MockConf: A Student Interpretation Dataset: Analysis, Word- and Span-level Alignment and Baselines](https://mlsanity.com/paper/2506.04848/) (2025)
- [From Black Box to Transparency: Enhancing Automated Interpreting Assessment with Explainable AI in College Classrooms](https://mlsanity.com/paper/2508.10860/) (2025)

## Abstract (arXiv)

Simultaneous interpretation, translation of the spoken word in real-time, is both highly challenging and physically demanding. Methods to predict interpreter confidence and the adequacy of the interpreted message have a number of potential applications, such as in computer-assisted interpretation interfaces or pedagogical tools. We propose the task of predicting simultaneous interpreter performance by building on existing methodology for quality estimation (QE) of machine translation output. In experiments over five settings in three language pairs, we extend a QE pipeline to estimate interpreter performance (as approximated by the METEOR evaluation metric) and propose novel features reflecting interpretation strategy and evaluation measures that further improve prediction accuracy.