# Backtranslation Feedback Improves User Confidence in MT, Not Quality

arXiv: https://arxiv.org/abs/2104.05688 · HTML: https://mlsanity.com/paper/2104.05688/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence-Level Knowledge Distillation](https://mlsanity.com/paper/1606.07947/) (2016)
- [Unsupervised Quality Estimation for Neural Machine Translation](https://mlsanity.com/paper/2005.10608/) (2020)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://mlsanity.com/paper/1803.05567/) (2018)
- [Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords](https://mlsanity.com/paper/2007.03006/) (2020)
- [ELITR Non-Native Speech Translation at IWSLT 2020](https://mlsanity.com/paper/2006.03331/) (2020)
- [Outbound Translation User Interface Ptakopet: A Pilot Study](https://mlsanity.com/paper/1911.10835/) (2019)
- [English-Czech Systems in WMT19: Document-Level Transformer](https://mlsanity.com/paper/1907.12750/) (2019)

## Built on by (most cited first, 10 of 10)

- [A Diachronic Perspective on User Trust in AI under Uncertainty](https://mlsanity.com/paper/2310.13544/) (2023)
- [RELIC: Investigating Large Language Model Responses using Self-Consistency](https://mlsanity.com/paper/2311.16842/) (2023)
- [Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors](https://mlsanity.com/paper/2310.16924/) (2023)
- [Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference](https://mlsanity.com/paper/2301.09008/) (2023)
- [Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactions](https://mlsanity.com/paper/2502.13780/) (2025)
- [QE4PE: Word-level Quality Estimation for Human Post-Editing](https://mlsanity.com/paper/2503.03044/) (2025)
- [An Interdisciplinary Approach to Human-Centered Machine Translation](https://mlsanity.com/paper/2506.13468/) (2025)
- [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://mlsanity.com/paper/2505.24683/) (2025)
- [Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust](https://mlsanity.com/paper/2205.06920/) (2022)
- [Measuring User's Mental Models of Speech Translation in Human-AI Collaboration](https://mlsanity.com/paper/2606.24644/) (2026)

## Abstract (arXiv)

Translating text into a language unknown to the text's author, dubbed outbound translation, is a modern need for which the user experience has significant room for improvement, beyond the basic machine translation facility. We demonstrate this by showing three ways in which user confidence in the outbound translation, as well as its overall final quality, can be affected: backward translation, quality estimation (with alignment) and source paraphrasing. In this paper, we describe an experiment on outbound translation from English to Czech and Estonian. We examine the effects of each proposed feedback module and further focus on how the quality of machine translation systems influence these findings and the user perception of success. We show that backward translation feedback has a mixed effect on the whole process: it increases user confidence in the produced translation, but not the objective quality.