# Bridging the Usability Gap: Lessons from Interpreting Studies for Machine Interpreting Design

arXiv: https://arxiv.org/abs/2606.16009 · HTML: https://mlsanity.com/paper/2606.16009/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 14 Jun 2026.

## Builds on (most cited first, 21 of 21)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer](https://mlsanity.com/paper/1803.06535/) (2018)
- [SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images](https://mlsanity.com/paper/2301.04883/) (2023)
- [Agentic Large Language Models, a survey](https://mlsanity.com/paper/2503.23037/) (2025)
- [Monotonic Infinite Lookback Attention for Simultaneous Machine Translation](https://mlsanity.com/paper/1906.05218/) (2019)
- [Learning to Translate in Real-time with Neural Machine Translation](https://mlsanity.com/paper/1610.00388/) (2016)
- [Speech Translation and the End-to-End Promise: Taking Stock of Where We Are](https://mlsanity.com/paper/2004.06358/) (2020)
- [Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description](https://mlsanity.com/paper/1710.07177/) (2017)
- [Polite Dialogue Generation Without Parallel Data](https://mlsanity.com/paper/1805.03162/) (2018)
- [Probing the Need for Visual Context in Multimodal Machine Translation](https://mlsanity.com/paper/1903.08678/) (2019)
- [Neural Dubber: Dubbing for Videos According to Scripts](https://mlsanity.com/paper/2110.08243/) (2021)
- [Bandit Structured Prediction for Neural Sequence-to-Sequence Learning](https://mlsanity.com/paper/1704.06497/) (2017)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent](https://mlsanity.com/paper/2407.21646/) (2024)
- [MT Metrics Correlate with Human Ratings of Simultaneous Speech Translation](https://mlsanity.com/paper/2211.08633/) (2022)
- [Towards the evaluation of automatic simultaneous speech translation from a communicative perspective](https://mlsanity.com/paper/2103.08364/) (2021)
- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)
- [Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023](https://mlsanity.com/paper/2309.15554/) (2023)
- [Exploring the Correlation between Human and Machine Evaluation of Simultaneous Speech Translation](https://mlsanity.com/paper/2406.10091/) (2024)

## Abstract (arXiv)

Machine interpreting (MI), the live, real-time application of speech translation, has achieved remarkable progress on standard benchmarks, with some systems approaching human parity on textual fidelity. Yet the user experience remains far inferior to interpreter-mediated communication, revealing what we term the accuracy illusion: systems that appear accurate on paper but fail in practice to support smooth, goal-oriented interaction. This paper defines MI as a distinct subfield of speech translation, with its own characteristics and the need for evaluation methods grounded in communicative effectiveness rather than isolated fidelity metrics. Drawing on insights from interpreting studies, we identify critical dimensions of professional interpreting practice that are overlooked by current systems, and consolidate them into three interdependent design priorities for future MI: agency (context-sensitive initiative and repair), grounding (multimodal and discourse-level situational awareness), and experience (adaptive improvement through real interaction). Together, these priorities chart a path toward closing the usability gap and enabling systems that can sustain authentic multilingual communication in real time.