Domain-Specific Quality Estimation for Machine Translation in Low-Resource Scenarios
Quality Estimation (QE) is essential for assessing machine translation quality in reference-less settings, particularly for domain-specific and low-resource language scenarios. In this paper, we investigate sentence-level QE for English to Indic machine translation across four domains (Healthcare, Legal, Tourism, and General) and five language pairs. We systematically compare zero-shot, few-shot, and guideline-anchored prompting across selected closed-weight and open-weight LLMs. Findings indicate that while closed-weight models achieve strong performance via prompting alone, prompt-only approaches remain fragile for open-weight models, especially in high-risk domains. To address this, we...
| Authors | Namrata Patil Gurav, Akashdeep Ranu, Archchana Sindhujan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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