5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs

• URL: http://arxiv.org/abs/2609.09964v1
• Date: Wed, 09 Sep 2026 09:52:19 GMT
• Title: 5-Dialects-BN: Unmasking the Impact of Transliteration on Bangla Dialectal LLMs
• Abstract summary: 5-Dialects-BN is the first multi-annotation Bangla dialect benchmark.<n> dataset comprises 6,000 manually annotated entries spanning five major dialects.
• Score: 0.45740558095423056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply for low-resource languages and dialectally diverse settings. Bangla, the world's sixth most spoken language, exemplifies this gap: existing resources overwhelmingly target Standard Bangla, leaving its regional dialects without the benchmarks needed to develop or evaluate dialect-aware systems. We address this gap with 5-Dialects-BN, the first multi-annotation Bangla dialect benchmark to align Romanized transliteration with dialectal text, Standard Bangla, English, and subjectivity labels across five regional varieties. The dataset comprises 6,000 manually annotated entries spanning five major dialects: Chittagong, Barisal, Noakhali, Sylhet, and Rangpur (Chittagong 1,900; Noakhali 1,500; Sylhet 1,200; Barisal 700; Rangpur 700), reflecting natural online availability. Each entry is enriched with five aligned annotations: the original dialectal text, a Romanized transliteration, an English translation, a Standard Bangla translation, and a subjectivity label (subjective vs. objective). Annotations were produced and cross-validated by native speakers and undergraduate linguistics students to ensure dialectal authenticity and semantic fidelity. The resulting resource supports a diverse suite of tasks, including dialect identification, dialect-to-standard normalization, machine translation, subjectivity classification, and parameter-efficient fine-tuning (e.g., LoRA) of multilingual LLMs. By providing a standardized, multi-annotation benchmark, 5-Dialects-BN enables principled evaluation of LLMs on dialectally diverse Bangla and lays a foundation for further research in low-resource, dialect-aware NLP.

Related papers

• Poly-Dialectal Neural Machine Translation System for Bangla Regional Dialects [0.0]
  Regional dialectal variation poses a fundamental challenge to natural language processing (NLP) in Bangla.<n>Over 240 million speakers communicate across diverse regional variants that diverge significantly from Standard Colloquial Bangla.<n>We present a unified Poly-Dialectal Neural Machine Translation System capable of multi-directional translation across 12 Bangla regional dialects.
  arXiv  Detail & Related papers  (2026-08-12T12:55:54Z)
• INDIC DIALECT: A Multi Task Benchmark to Evaluate and Translate in Indian Language Dialects [10.663878830823043]
  In India, despite Hindi being the third most spoken language globally (over 600 million speakers), its numerous dialects remain underrepresented.<n>We introduce INDIC-DIALECT, a human-curated parallel corpus of 13k sentence pairs spanning 11 dialects and 2 languages: Hindi and Odia.
  arXiv  Detail & Related papers  (2026-01-15T13:40:27Z)
• XIFBench: Evaluating Large Language Models on Multilingual Instruction Following [59.549015333755186]
  Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications.<n>Existing evaluations lack fine-grained constraint analysis across diverse linguistic contexts.<n>We introduce XIFBench, a comprehensive benchmark for evaluating multilingual instruction-following abilities of LLMs.
  arXiv  Detail & Related papers  (2025-03-10T17:07:52Z)
• Bridging Dialects: Translating Standard Bangla to Regional Variants Using Neural Models [1.472830326343432]
  The work is motivated by the need to preserve linguistic diversity and improve communication among dialect speakers.<n>The models were fine-tuned using the "Vashantor" dataset, containing 32,500 sentences across various dialects.<n> BanglaT5 demonstrated superior performance with a CER of 12.3% and WER of 15.7%, highlighting its effectiveness in capturing dialectal nuances.
  arXiv  Detail & Related papers  (2025-01-10T06:50:51Z)
• BanglaDialecto: An End-to-End AI-Powered Regional Speech Standardization [7.059964549363294]
  The study presents an end-to-end pipeline for converting dialectal Noakhali speech to standard Bangla speech. Being the fifth most spoken language with around 55 distinct dialects spoken by 160 million people, addressing Bangla dialects is crucial for developing inclusive communication tools. Our experiments demonstrated that fine-tuning the Whisper ASR model achieved a CER of 0.8% and WER of 1.5%, while the BanglaT5 model attained a BLEU score of 41.6% for dialect-to-standard text translation.
  arXiv  Detail & Related papers  (2024-11-16T20:20:15Z)
• BanglaLlama: LLaMA for Bangla Language [1.0710988917914002]
  Despite being the 5th largest spoken language in the world, Bangla is still a "low-resource" language.<n>Existing pretrained language models often struggle to perform well on Bangla Language Processing (BLP) tasks.<n>This paper introduces two high-quality translated Bangla-instruction datasets totaling 224k samples.
  arXiv  Detail & Related papers  (2024-10-28T16:44:02Z)
• Voices Unheard: NLP Resources and Models for Yorùbá Regional Dialects [72.18753241750964]
  Yorub'a is an African language with roughly 47 million speakers. Recent efforts to develop NLP technologies for African languages have focused on their standard dialects. We take steps towards bridging this gap by introducing a new high-quality parallel text and speech corpus.
  arXiv  Detail & Related papers  (2024-06-27T22:38:04Z)
• What Do Dialect Speakers Want? A Survey of Attitudes Towards Language Technology for German Dialects [60.8361859783634]
  We survey speakers of dialects and regional languages related to German. We find that respondents are especially in favour of potential NLP tools that work with dialectal input.
  arXiv  Detail & Related papers  (2024-02-19T09:15:28Z)
• 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)
• Task-Agnostic Low-Rank Adapters for Unseen English Dialects [52.88554155235167]
  Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English. By disentangling dialect-specific and cross-dialectal information, HyperLoRA improves generalization to unseen dialects in a task-agnostic fashion.
  arXiv  Detail & Related papers  (2023-11-02T01:17:29Z)
• Multi-VALUE: A Framework for Cross-Dialectal English NLP [49.55176102659081]
  Multi- Dialect is a controllable rule-based translation system spanning 50 English dialects. Stress tests reveal significant performance disparities for leading models on non-standard dialects. We partner with native speakers of Chicano and Indian English to release new gold-standard variants of the popular CoQA task.
  arXiv  Detail & Related papers  (2022-12-15T18:17:01Z)
• VALUE: Understanding Dialect Disparity in NLU [50.35526025326337]
  We construct rules for 11 features of African American Vernacular English (AAVE) We recruit fluent AAVE speakers to validate each feature transformation via linguistic acceptability judgments. Experiments show that these new dialectal features can lead to a drop in model performance.
  arXiv  Detail & Related papers  (2022-04-06T18:30:56Z)

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.