# Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation

arXiv: https://arxiv.org/abs/2408.17308 · HTML: https://mlsanity.com/paper/2408.17308/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [COMET: A Neural Framework for MT Evaluation](https://mlsanity.com/paper/2009.09025/) (2020)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016)
- [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021)
- [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019)
- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation](https://mlsanity.com/paper/1808.10432/) (2018)
- [Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation](https://mlsanity.com/paper/1906.12068/) (2019)
- [A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation](https://mlsanity.com/paper/1806.06957/) (2018)
- [Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification](https://mlsanity.com/paper/2109.07604/) (2021)
- [Unsupervised Identification of Translationese](https://mlsanity.com/paper/1609.03205/) (2016)
- [T5QL: Taming language models for SQL generation](https://mlsanity.com/paper/2209.10254/) (2022)
- [What's the Difference Between Professional Human and Machine Translation? A Blind Multi-language Study on Domain-specific MT](https://mlsanity.com/paper/2006.04781/) (2020)
- [Translating away Translationese without Parallel Data](https://mlsanity.com/paper/2310.18830/) (2023)
- [Towards Debiasing Translation Artifacts](https://mlsanity.com/paper/2205.08001/) (2022)

## Built on by (most cited first, 5 of 5)

- [Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation](https://mlsanity.com/paper/2412.08473/) (2024)
- [DataPrep-Bench: Benchmarking LLMs as Training Data Preparators](https://mlsanity.com/paper/2607.20465/) (2026)
- [Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children's Literature Translation](https://mlsanity.com/paper/2506.22038/) (2025)
- [On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation](https://mlsanity.com/paper/2601.13729/) (2026)
- [Metaphors in Literary Post-Editing: Opening Pandora's Box?](https://mlsanity.com/paper/2605.21178/) (2026)

## Abstract (arXiv)

Machine translations are found to be lexically poorer than human translations. The loss of lexical diversity through MT poses an issue in the automatic translation of literature, where it matters not only what is written, but also how it is written. Current methods for increasing lexical diversity in MT are rigid. Yet, as we demonstrate, the degree of lexical diversity can vary considerably across different novels. Thus, rather than aiming for the rigid increase of lexical diversity, we reframe the task as recovering what is lost in the machine translation process. We propose a novel approach that consists of reranking translation candidates with a classifier that distinguishes between original and translated text. We evaluate our approach on 31 English-to-Dutch book translations, and find that, for certain books, our approach retrieves lexical diversity scores that are close to human translation.