# Original or Translated? On the Use of Parallel Data for Translation Quality Estimation

arXiv: https://arxiv.org/abs/2212.10257 · HTML: https://mlsanity.com/paper/2212.10257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Tagged Back-Translation](https://mlsanity.com/paper/1906.06442/) (2019)
- [TransQuest: Translation Quality Estimation with Cross-lingual Transformers](https://mlsanity.com/paper/2011.01536/) (2020)
- [Understanding and Improving Lexical Choice in Non-Autoregressive Translation](https://mlsanity.com/paper/2012.14583/) (2020)
- [The Effect of Translationese in Machine Translation Test Sets](https://mlsanity.com/paper/1906.08069/) (2019)
- [Translationese in Machine Translation Evaluation](https://mlsanity.com/paper/1906.09833/) (2019)
- [Translationese as a Language in "Multilingual" NMT](https://mlsanity.com/paper/1911.03823/) (2019)
- [Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation](https://mlsanity.com/paper/2106.00903/) (2021)
- [Post-editese: an Exacerbated Translationese](https://mlsanity.com/paper/1907.00900/) (2019)
- [Context-Aware Cross-Attention for Non-Autoregressive Translation](https://mlsanity.com/paper/2011.00770/) (2020)
- [On the Language Coverage Bias for Neural Machine Translation](https://mlsanity.com/paper/2106.03297/) (2021)

## Built on by (most cited first, 2 of 2)

- [Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models](https://mlsanity.com/paper/2303.13809/) (2023)
- [Tailoring Domain Adaptation for Machine Translation Quality Estimation](https://mlsanity.com/paper/2304.08891/) (2023)

## Abstract (arXiv)

Machine Translation Quality Estimation (QE) is the task of evaluating translation output in the absence of human-written references. Due to the scarcity of human-labeled QE data, previous works attempted to utilize the abundant unlabeled parallel corpora to produce additional training data with pseudo labels. In this paper, we demonstrate a significant gap between parallel data and real QE data: for QE data, it is strictly guaranteed that the source side is original texts and the target side is translated (namely translationese). However, for parallel data, it is indiscriminate and the translationese may occur on either source or target side. We compare the impact of parallel data with different translation directions in QE data augmentation, and find that using the source-original part of parallel corpus consistently outperforms its target-original counterpart. Moreover, since the WMT corpus lacks direction information for each parallel sentence, we train a classifier to distinguish source- and target-original bitext, and carry out an analysis of their difference in both style and domain. Together, these findings suggest using source-original parallel data for QE data augmentation, which brings a relative improvement of up to 4.0% and 6.4% compared to undifferentiated data on sentence- and word-level QE tasks respectively.