# SentiMaithili: A Benchmark Dataset for Sentiment and Reason Generation for the Low-Resource Maithili Language

arXiv: https://arxiv.org/abs/2510.22160 · HTML: https://mlsanity.com/paper/2510.22160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 16 of 16)

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- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
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- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [IndicBART: A Pre-trained Model for Indic Natural Language Generation](https://mlsanity.com/paper/2109.02903/) (2021)
- [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://mlsanity.com/paper/2004.07807/) (2020)
- [BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories](https://mlsanity.com/paper/1910.04073/) (2019)
- [Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications](https://mlsanity.com/paper/2004.13945/) (2020)
- [Enhancing Multilingual Sentiment Analysis with Explainability for Sinhala, English, and Code-Mixed Content](https://mlsanity.com/paper/2504.13545/) (2025)
- [A Finite State Transducer Based Morphological Analyzer of Maithili Language](https://mlsanity.com/paper/2003.00234/) (2020)

## Abstract (arXiv)

Developing benchmark datasets for low-resource languages poses significant challenges, primarily due to the limited availability of native linguistic experts and the substantial time and cost involved in annotation. Given these challenges, Maithili is still underrepresented in natural language processing research. It is an Indo-Aryan language spoken by more than 13 million people in the Purvanchal region of India, valued for its rich linguistic structure and cultural significance. While sentiment analysis has achieved remarkable progress in high-resource languages, resources for low-resource languages, such as Maithili, remain scarce, often restricted to coarse-grained annotations and lacking interpretability mechanisms. To address this limitation, we introduce a novel dataset comprising 3,221 Maithili sentences annotated for sentiment polarity and accompanied by natural language justifications. Moreover, the dataset is carefully curated and validated by linguistic experts to ensure both label reliability and contextual fidelity. Notably, the justifications are written in Maithili, thereby promoting culturally grounded interpretation and enhancing the explainability of sentiment models. Furthermore, extensive experiments using both classical machine learning and state-of-the-art transformer architectures demonstrate the dataset's effectiveness for interpretable sentiment analysis. Ultimately, this work establishes the first benchmark for explainable affective computing in Maithili, thus contributing a valuable resource to the broader advancement of multilingual NLP and explainable AI.