Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages

• URL: http://arxiv.org/abs/2607.12612v1
• Date: Tue, 14 Jul 2026 10:44:45 GMT
• Title: Translation as a Computationally Efficient Bridge: Feasibility of English BERT for Low-Resource Languages
• Abstract summary: BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains.<n>High-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands.<n>This study compares translation-based fine-tuning with native-language BERT performance across tasks and languages.
• Score: 1.3899485276817554
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, developing high-quality BERT models for non-English languages remains challenging due to limited annotated data and high computational demands. Translating non-English data into English and fine-tuning existing English BERT models offers a resource-efficient alternative, yet few studies have structurally compared translation-based fine-tuning with native-language BERT performance across tasks and languages. This study provides such a comparison, evaluating the feasibility of translation-based fine-tuning across six NLP tasks: Sentiment Analysis, Hate Speech Detection, Question Answering, Named Entity Recognition, Part-of-Speech Tagging, and Natural Language Inference, using datasets translated from Bulgarian, Chinese, Dutch, Italian, and Russian. Across all settings, the translation-based approach was comparable or superior in 53.3 percent of cases. Gains were most frequent in Question Answering, Part-of-Speech Tagging, and Natural Language Inference, while performance declines were common in Named Entity Recognition and Hate Speech Detection. The results show that translation-based fine-tuning is most effective for tasks relying on syntactic or structural patterns and for languages typologically close to English, such as Dutch, but less effective for token-level or culturally nuanced tasks, particularly in Chinese. Overall, this study demonstrates that translation-based fine-tuning offers a scalable, resource-efficient, and empirically validated path for extending NLP to low-resource languages while advancing linguistic inclusivity and sustainability in artificial intelligence.

Related papers

• Equity with Efficiency: An Empirical Study of Tokenizers for Multilingual Large Language Models [30.7730386396743]
  Byte-level Byte-Pair tokenizers structurally favor high-resource languages and Latin scripts.<n>For speakers of underrepresented languages, this bias inflates inference costs and widens cross-lingual capability gaps.<n>We present the first systematic comparison of equitable tokenizers on a benchmark spanning 11 Southeast Asian languages.
  arXiv  Detail & Related papers  (2026-06-13T01:10:42Z)
• Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation? [49.7935995447581]
  We investigate whether low-resource machine translation can benefit from structured intermediate steps of linguistic analysis and grammatical reasoning.<n>We propose a pipeline for automatically generating step-by-step linguistic reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar-rule banks.<n>Our results show that linguistic reasoning traces are most effective as inference-time guidance.
  arXiv  Detail & Related papers  (2026-06-02T15:36:12Z)
• Semi-Synthetic Parallel Data for Translation Quality Estimation: A Case Study of Dataset Building for an Under-Resourced Language Pair [0.0]
  This study presents a semi-synthetic parallel dataset for English-to-Hebrew QE.<n>We incorporated professionally translated English-Hebrew segments from our own resources, which were assigned the highest quality score.<n> Controlled translation errors were introduced to address linguistic challenges, particularly regarding gender and number agreement.
  arXiv  Detail & Related papers  (2026-03-12T09:48:34Z)
• Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks [6.177998679139308]
  Despite advances in Neural Machine Translation (NMT), low-resource languages like Tigrinya remain underserved.<n>This paper investigates transfer learning techniques using multilingual pretrained models to enhance translation quality for morphologically rich, low-resource languages.
  arXiv  Detail & Related papers  (2025-09-24T15:02:57Z)
• A Data Selection Approach for Enhancing Low Resource Machine Translation Using Cross-Lingual Sentence Representations [0.4499833362998489]
  This study focuses on the case of English-Marathi language pairs, where existing datasets are notably noisy. To mitigate the impact of data quality issues, we propose a data filtering approach based on cross-lingual sentence representations. Results demonstrate a significant improvement in translation quality over the baseline post-filtering with IndicSBERT.
  arXiv  Detail & Related papers  (2024-09-04T13:49:45Z)
• Natural Language Processing for Dialects of a Language: A Survey [56.93337350526933]
  State-of-the-art natural language processing (NLP) models are trained on massive training corpora, and report a superlative performance on evaluation datasets.<n>This survey delves into an important attribute of these datasets: the dialect of a language.<n>Motivated by the performance degradation of NLP models for dialectal datasets and its implications for the equity of language technologies, we survey past research in NLP for dialects in terms of datasets, and approaches.
  arXiv  Detail & Related papers  (2024-01-11T03:04:38Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation [113.99145386490639]
  Cross-lingual NER can transfer knowledge between languages via aligned cross-lingual representations or machine translation results. We propose a Cross-lingual Entity Projection framework (CROP) to enable zero-shot cross-lingual NER. We adopt a multilingual labeled sequence translation model to project the tagged sequence back to the target language and label the target raw sentence.
  arXiv  Detail & Related papers  (2022-10-13T13:32:36Z)
• BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives [0.0]
  BERT has revolutionized the NLP field by enabling transfer learning with large language models. This article studies how to better cope with the different embeddings provided by the BERT output layer and the usage of language-specific instead of multilingual models.
  arXiv  Detail & Related papers  (2022-01-10T15:05:05Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)
• A Study of Cross-Lingual Ability and Language-specific Information in Multilingual BERT [60.9051207862378]
  multilingual BERT works remarkably well on cross-lingual transfer tasks. Datasize and context window size are crucial factors to the transferability. There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
  arXiv  Detail & Related papers  (2020-04-20T11:13:16Z)

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.