Get away with less: Need of source side data curation to build parallel corpus for low resource Machine Translation

• URL: http://arxiv.org/abs/2601.08629v1
• Date: Tue, 13 Jan 2026 15:05:19 GMT
• Title: Get away with less: Need of source side data curation to build parallel corpus for low resource Machine Translation
• Authors: Saumitra Yadav, Manish Shrivastava,
• Abstract summary: We develop framework that screens source sentences to form efficient parallel text.<n>We find that by training mostly on complex sentences from both existing and synthetic datasets, our method significantly improves translation quality.<n>This approach not only reduces MT systems training cost by reducing training data requirement, but also showcases LALITA's utility in data augmentation.
• Score: 3.3393607383304253
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data curation is a critical yet under-researched step in the machine translation training paradigm. To train translation systems, data acquisition relies primarily on human translations and digital parallel sources or, to a limited degree, synthetic generation. But, for low-resource languages, human translation to generate sufficient data is prohibitively expensive. Therefore, it is crucial to develop a framework that screens source sentences to form efficient parallel text, ensuring optimal MT system performance in low-resource environments. We approach this by evaluating English-Hindi bi-text to determine effective sentence selection strategies for optimal MT system training. Our extensively tested framework, (Lexical And Linguistically Informed Text Analysis) LALITA, targets source sentence selection using lexical and linguistic features to curate parallel corpora. We find that by training mostly on complex sentences from both existing and synthetic datasets, our method significantly improves translation quality. We test this by simulating low-resource data availabilty with curated datasets of 50K to 800K English sentences and report improved performances on all data sizes. LALITA demonstrates remarkable efficiency, reducing data needs by more than half across multiple languages (Hindi, Odia, Nepali, Norwegian Nynorsk, and German). This approach not only reduces MT systems training cost by reducing training data requirement, but also showcases LALITA's utility in data augmentation.

Related papers

• Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation [49.82863380286994]
  In-context learning may offer novel ways to adapt Large Language Models for low-resource machine translation.<n>In this study, we explore scaling low-resource machine translation ICL beyond the few-shot setting to thousands of examples with long-context models.<n>Our experiments on Javanese and Sundanese show that gains from additional context saturate quickly and can degrade near the maximum context window.
  arXiv  Detail & Related papers  (2026-02-04T17:02:22Z)
• KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization [64.1520245849231]
  This paper presents KIT's submissions to the IWSLT 2025 low-resource track.<n>We develop both cascaded systems, and end-to-end (E2E) Speech Translation systems.<n>Building upon pre-trained models, we fine-tune our systems with different strategies to utilize resources efficiently.
  arXiv  Detail & Related papers  (2025-05-26T08:38:02Z)
• Scaling Low-Resource MT via Synthetic Data Generation with LLMs [17.430071094867532]
  We construct a document-level synthetic corpus from English Europarl.<n>We study its practical application by identifying effective training regimes.<n>We introduce SynOPUS, a public repository for synthetic parallel datasets.
  arXiv  Detail & Related papers  (2025-05-20T14:31:54Z)
• Trans-Zero: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data [64.4458540273004]
  We propose a self-play framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs)<n>Experiments demonstrate that this approach not only matches the performance of models trained on large-scale parallel data but also excels in non-English translation directions.
  arXiv  Detail & Related papers  (2025-04-20T16:20:30Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili [1.9686054517684888]
  In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available. We study and apply three simple data augmentation techniques popularly used in text classification tasks. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.
  arXiv  Detail & Related papers  (2023-06-12T20:43:24Z)
• OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval [91.76575626229824]
  We present OneAligner, an alignment model specially designed for sentence retrieval tasks. When trained with all language pairs of a large-scale parallel multilingual corpus (OPUS-100), this model achieves the state-of-the-art result. We conclude through empirical results and analyses that the performance of the sentence alignment task depends mostly on the monolingual and parallel data size.
  arXiv  Detail & Related papers  (2022-05-17T19:52:42Z)
• Distributionally Robust Multilingual Machine Translation [94.51866646879337]
  We propose a new learning objective for Multilingual neural machine translation (MNMT) based on distributionally robust optimization. We show how to practically optimize this objective for large translation corpora using an iterated best response scheme. Our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.
  arXiv  Detail & Related papers  (2021-09-09T03:48:35Z)
• Multilingual Neural Semantic Parsing for Low-Resourced Languages [1.6244541005112747]
  We introduce a new multilingual semantic parsing dataset in English, Italian and Japanese. We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset. We find that a semantic trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
  arXiv  Detail & Related papers  (2021-06-07T09:53:02Z)
• Selecting Backtranslated Data from Multiple Sources for Improved Neural Machine Translation [8.554761233491236]
  We analyse the impact that data translated with rule-based, phrase-based statistical and neural MT systems has on new MT systems. We exploit different data selection strategies in order to reduce the amount of data used, while at the same time maintaining high-quality MT systems.
  arXiv  Detail & Related papers  (2020-05-01T10:50:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.