# Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation

arXiv: https://arxiv.org/abs/2304.11501 · HTML: https://mlsanity.com/paper/2304.11501/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 62 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 19 of 19)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [The Effect of Translationese in Machine Translation Test Sets](https://mlsanity.com/paper/1906.08069/) (2019)
- [Personalized Machine Translation: Preserving Original Author Traits](https://mlsanity.com/paper/1610.05461/) (2016)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://mlsanity.com/paper/1906.00565/) (2019)
- [Semantic Neural Machine Translation using AMR](https://mlsanity.com/paper/1902.07282/) (2019)
- [Post-editese: an Exacerbated Translationese](https://mlsanity.com/paper/1907.00900/) (2019)
- [Can Automatic Post-Editing Improve NMT?](https://mlsanity.com/paper/2009.14395/) (2020)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification](https://mlsanity.com/paper/2109.07604/) (2021)
- [Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation](https://mlsanity.com/paper/1906.05683/) (2019)
- [Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance](https://mlsanity.com/paper/2205.02293/) (2022)
- [Controlling Styles in Neural Machine Translation with Activation Prompt](https://mlsanity.com/paper/2212.08909/) (2022)
- [T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation](https://mlsanity.com/paper/2212.01667/) (2022)
- [On the Similarities Between Native, Non-native and Translated Texts](https://mlsanity.com/paper/1609.03204/) (2016)

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

- [Lost in Literalism: How Supervised Training Shapes Translationese in LLMs](https://mlsanity.com/paper/2503.04369/) (2025)
- [Sentence Smith: Controllable Edits for Evaluating Text Embeddings](https://mlsanity.com/paper/2502.14734/) (2025)
- [Survey of Abstract Meaning Representation: Then, Now, Future](https://mlsanity.com/paper/2505.03229/) (2025)
- [NeoBabel: A Multilingual Open Tower for Visual Generation](https://mlsanity.com/paper/2507.06137/) (2025)
- [Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation](https://mlsanity.com/paper/2412.08473/) (2024)
- [When Does Meaning Backfire? Investigating the Role of AMRs in NLI](https://mlsanity.com/paper/2506.14613/) (2025)
- [Speaking of Language: Reflections on Metalanguage Research in NLP](https://mlsanity.com/paper/2604.02645/) (2026)
- [Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora](https://mlsanity.com/paper/2605.22660/) (2026)
- [Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding](https://mlsanity.com/paper/2505.08504/) (2025)
- [ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue](https://mlsanity.com/paper/2508.12819/) (2025)

## Abstract (arXiv)

Translated texts bear several hallmarks distinct from texts originating in the language. Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language ("translationese") and can affect model performance. We frame the novel task of translationese reduction and hypothesize that Abstract Meaning Representation (AMR), a graph-based semantic representation which abstracts away from the surface form, can be used as an interlingua to reduce the amount of translationese in translated texts. By parsing English translations into an AMR and then generating text from that AMR, the result more closely resembles originally English text across three quantitative macro-level measures, without severely compromising fluency or adequacy. We compare our AMR-based approach against three other techniques based on machine translation or paraphrase generation. This work makes strides towards reducing translationese in text and highlights the utility of AMR as an interlingua.