NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data

• URL: http://arxiv.org/abs/2512.12537v1
• Date: Sun, 14 Dec 2025 04:08:26 GMT
• Title: NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data
• Authors: Agniva Maiti, Manya Pandey, Murari Mandal,
• Abstract summary: NagaNLP is a comprehensive open-source toolkit for Nagamese.<n>It relies on LLM-driven but human-validated synthetic data generation.<n>We train both discriminative and generative models.
• Score: 6.689013010749215
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The vast majority of the world's languages, particularly creoles like Nagamese, remain severely under-resourced in Natural Language Processing (NLP), creating a significant barrier to their representation in digital technology. This paper introduces NagaNLP, a comprehensive open-source toolkit for Nagamese, bootstrapped through a novel methodology that relies on LLM-driven but human-validated synthetic data generation. We detail a multi-stage pipeline where an expert-guided LLM (Gemini) generates a candidate corpus, which is then refined and annotated by native speakers. This synthetic-hybrid approach yielded a 10K pair conversational dataset and a high-quality annotated corpus for foundational tasks. To assess the effectiveness of our methodology, we trained both discriminative and generative models. Our fine-tuned XLM-RoBERTa-base model establishes a new benchmark for Nagamese, achieving a 93.81\% accuracy (0.90 F1-Macro) on Part-of-Speech tagging and a 0.75 F1-Macro on Named Entity Recognition, massively outperforming strong zero-shot baselines. Furthermore, we fine-tuned a Llama-3.2-3B Instruct model, named NagaLLaMA, which demonstrates superior performance on conversational tasks, achieving a Perplexity of 3.85, an order of magnitude improvement over its few-shot counterpart (96.76). We release the NagaNLP toolkit, including all datasets, models, and code, providing a foundational resource for a previously underserved language and a reproducible framework for reducing data scarcity in other low-resource contexts.

Related papers

• HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language [1.3465808629549525]
  This paper introduces a novel benchmark dataset comprising 5,000 YouTube comments in Hausa and code-switched English.<n>We use this dataset to conduct a comparative analysis of classical models and fine-tuned transformer models.<n>Our results reveal a key finding: the Decision Tree classifier, with an accuracy and F1-score 89.72% and 89.60% respectively, significantly outperformed the deep learning models.
  arXiv  Detail & Related papers  (2025-09-17T22:57:21Z)
• Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese [12.208154616426052]
  We test whether large language models (LLMs) can generate culturally nuanced narratives in Javanese and Sundanese.<n>We compare three data creation strategies: (1) LLM-assisted stories prompted with cultural cues, (2) machine translation from Indonesian benchmarks, and (3) native-written stories.<n>We fine-tune models on each dataset and evaluate on a human-authored test set for classification and generation.
  arXiv  Detail & Related papers  (2025-02-18T15:14:58Z)
• Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek [2.3499129784547663]
  We evaluate the performance of open-source (Llama-70b) and closed-source (GPT-4o mini) large language models on seven core NLP tasks with dataset availability.<n>Second, we expand the scope of Greek NLP by reframing Authorship Attribution as a tool to assess potential data usage by LLMs in pre-training.<n>Third, we showcase a legal NLP case study, where a Summarize, Translate, and Embed (STE) methodology outperforms the traditional TF-IDF approach for clustering emphlong legal texts.
  arXiv  Detail & Related papers  (2025-01-22T12:06:16Z)
• Transformer-Based Contextualized Language Models Joint with Neural Networks for Natural Language Inference in Vietnamese [1.7457686843484872]
  We conduct experiments using various combinations of contextualized language models (CLM) and neural networks. We find that the joint approach of CLM and neural networks is simple yet capable of achieving high-quality performance.
  arXiv  Detail & Related papers  (2024-11-20T15:46:48Z)
• 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)
• YAYI 2: Multilingual Open-Source Large Language Models [53.92832054643197]
  We propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback.
  arXiv  Detail & Related papers  (2023-12-22T17:34:47Z)
• CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation [94.59630161324013]
  We propose CoAnnotating, a novel paradigm for Human-LLM co-annotation of unstructured texts at scale. Our empirical study shows CoAnnotating to be an effective means to allocate work from results on different datasets, with up to 21% performance improvement over random baseline.
  arXiv  Detail & Related papers  (2023-10-24T08:56:49Z)
• An Open Dataset and Model for Language Identification [84.15194457400253]
  We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages. We make both the model and the dataset available to the research community.
  arXiv  Detail & Related papers  (2023-05-23T08:43:42Z)
• ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval [22.882301169283323]
  We propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models.
  arXiv  Detail & Related papers  (2023-05-18T04:30:09Z)
• ZeroGen$^+$: Self-Guided High-Quality Data Generation in Efficient Zero-Shot Learning [97.2907428983142]
  ZeroGen attempts to purely use PLM to generate data and train a tiny model without relying on task-specific annotation. We propose a noise-robust bi-level re-weighting framework which is able to learn the per-sample weights measuring the data quality without requiring any gold data.
  arXiv  Detail & Related papers  (2022-05-25T11:38:48Z)
• WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation [101.00109827301235]
  We introduce a novel paradigm for dataset creation based on human and machine collaboration. We use dataset cartography to automatically identify examples that demonstrate challenging reasoning patterns, and instruct GPT-3 to compose new examples with similar patterns. The resulting dataset, WANLI, consists of 108,357 natural language inference (NLI) examples that present unique empirical strengths.
  arXiv  Detail & Related papers  (2022-01-16T03:13:49Z)
• Self-Learning for Zero Shot Neural Machine Translation [13.551731309506874]
  This work proposes a novel zero-shot NMT modeling approach that learns without the now-standard assumption of a pivot language sharing parallel data. Compared to unsupervised NMT, consistent improvements are observed even in a domain-mismatch setting.
  arXiv  Detail & Related papers  (2021-03-10T09:15:19Z)

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.