# As Little as Possible, as Much as Necessary: Detecting Over- and Undertranslations with Contrastive Conditioning

arXiv: https://arxiv.org/abs/2203.01927 · HTML: https://mlsanity.com/paper/2203.01927/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 3 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://mlsanity.com/paper/2011.02593/) (2020)
- [Modeling Coverage for Neural Machine Translation](https://mlsanity.com/paper/1601.04811/) (2016)
- [OpenKiwi: An Open Source Framework for Quality Estimation](https://mlsanity.com/paper/1902.08646/) (2019)
- [Quality Estimation without Human-labeled Data](https://mlsanity.com/paper/2102.04020/) (2021)
- [Neural Machine Translation with Adequacy-Oriented Learning](https://mlsanity.com/paper/1811.08541/) (2018)
- [Modeling Past and Future for Neural Machine Translation](https://mlsanity.com/paper/1711.09502/) (2017)
- [Otem&Utem: Over- and Under-Translation Evaluation Metric for NMT](https://mlsanity.com/paper/1807.08945/) (2018)

## Built on by (most cited first, 11 of 11)

- [Mitigating Hallucinations and Off-target Machine Translation with Source-Contrastive and Language-Contrastive Decoding](https://mlsanity.com/paper/2309.07098/) (2023)
- [Multi-property Steering of Large Language Models with Dynamic Activation Composition](https://mlsanity.com/paper/2406.17563/) (2024)
- [HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translation](https://mlsanity.com/paper/2305.11746/) (2023)
- [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://mlsanity.com/paper/2210.15615/) (2022)
- [Word Alignment as Preference for Machine Translation](https://mlsanity.com/paper/2405.09223/) (2024)
- [MMTE: Corpus and Metrics for Evaluating Machine Translation Quality of Metaphorical Language](https://mlsanity.com/paper/2406.13698/) (2024)
- [Quantifying the Plausibility of Context Reliance in Neural Machine Translation](https://mlsanity.com/paper/2310.01188/) (2023)
- [Are We Paying Attention to Her? Investigating Gender Disambiguation and Attention in Machine Translation](https://mlsanity.com/paper/2505.08546/) (2025)
- [Towards Fine-Grained Information: Identifying the Type and Location of Translation Errors](https://mlsanity.com/paper/2302.08975/) (2023)
- [M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation](https://mlsanity.com/paper/2510.13434/) (2025)
- [With a Little Help from the Authors: Reproducing Human Evaluation of an MT Error Detector](https://mlsanity.com/paper/2308.06527/) (2023)

## Abstract (arXiv)

Omission and addition of content is a typical issue in neural machine translation. We propose a method for detecting such phenomena with off-the-shelf translation models. Using contrastive conditioning, we compare the likelihood of a full sequence under a translation model to the likelihood of its parts, given the corresponding source or target sequence. This allows to pinpoint superfluous words in the translation and untranslated words in the source even in the absence of a reference translation. The accuracy of our method is comparable to a supervised method that requires a custom quality estimation model.