mlsanityData to 14 Oct 2026

Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin

arXiv 2407.08819 ↗First posted 11 Jul 2024cs.CLcited by 5

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Abstract (arXiv)

This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

15 of 34 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  3. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
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  8. 2023The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediationscited by 131
  9. 2021Neural Machine Translation for Low-Resource Languages: A Surveycited by 109
  10. 2021Survey of Low-Resource Machine Translationcited by 92
  11. 2019Tagged Back-Translationcited by 77
  12. 2019On The Evaluation of Machine Translation Systems Trained With Back-Translationcited by 40
3 more
  1. 2019Using Monolingual Data in Neural Machine Translation: a Systematic Studycited by 21
  2. 2019The Source-Target Domain Mismatch Problem in Machine Translationcited by 8
  3. 2023On Synthetic Data for Back Translationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translationcited by 8
  2. 2025Compensating for Data with Reasoning: Low-Resource Machine Translation with LLMscited by 2
  3. 2026Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varietiescited by 2
  4. 2025Exploring NLP Benchmarks in an Extremely Low-Resource Settingcited by 0
  5. 2026A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Bookscited by 0