MuRIL_WR

Model Description

MuRIL_WR is a Telugu sentiment classification model built on MuRIL (Multilingual Representations for Indian Languages), a Transformer-based BERT model specifically designed for Indian languages, including Telugu and English.

MuRIL is pretrained on a large and diverse corpus of Indian language text, including web data, religious scriptures, and news content. In contrast to general multilingual models such as mBERT and XLM-R, MuRIL is better suited to capture Telugu morphology, syntax, and linguistic structure.

The suffix WR denotes With Rationale supervision. This model is fine-tuned using both sentiment labels and human-annotated rationales, enabling improved alignment between model predictions and human-identified evidence.

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Pretraining Details

• Pretraining corpus: Indian language text from web sources, religious texts, and news data
• Training objectives:
  • Masked Language Modeling (MLM)
  • Translation Language Modeling (TLM)
• Language coverage: 17+ Indian languages, including Telugu and English

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Training Data

• Fine-tuning dataset: Telugu-Dataset
• Task: Sentiment classification
• Supervision type: Label + rationale supervision
• Rationales: Token-level human-annotated evidence spans

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Rationale Supervision

During fine-tuning, human-provided rationales guide model learning. Alongside the standard classification loss, an auxiliary rationale loss encourages the model’s attention or explanation scores to align with annotated rationale tokens.

This approach improves:

• Interpretability of sentiment predictions
• Alignment between model explanations and human judgment
• Plausibility of generated explanations

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Intended Use

This model is intended for:

• Explainable Telugu sentiment classification
• Rationale-supervised learning experiments
• Indian-language explainability research
• Comparative evaluation against label-only (WOR) baselines

MuRIL_WR is particularly effective for informal, conversational, and social media Telugu text, where rationale supervision further enhances explanation quality.

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Performance Characteristics

Compared to label-only training, rationale supervision typically improves explanation plausibility while maintaining competitive sentiment classification performance.

Strengths

• Strong Telugu-specific linguistic modeling
• Human-aligned explanations via rationale supervision
• Suitable for explainable AI benchmarking in Indian languages

Limitations

• Requires human-annotated rationales, increasing annotation effort
• Pretraining data bias toward informal text may affect formal Telugu tasks
• Classification gains over WOR may be modest

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Use in Explainability Evaluation

MuRIL_WR is well-suited for evaluation with explanation frameworks such as FERRET, enabling:

• Faithfulness evaluation: How well explanations support model predictions
• Plausibility evaluation: How closely explanations align with human rationales

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References

• Khanuja et al., 2021
• Joshi, 2022
• Das et al., 2022
• Rajalakshmi et al., 2023