arXiv Computation and Language September 1, 2026 By Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare, Daria Sokova, Shenbin Qian, Girish Koushik, Tharindu Ranasinghe, Constantin Or\u{a}san, Chrysoula Zerva, Ricardo Rei, Fr\'ed\'eric Blain, Andr\'e F. T. Martins, Marco Turchi, Matteo Negri, Anoop Kunchukuttan, Mitesh M. Khapra, Pushpak Bhattacharyya

IndicQE-APE: A Benchmark for Quality Estimation and Automatic Post-Editing for Indic Languages

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IndicQE-APE is a consolidated benchmark that unifies quality estimation (QE) and automatic post‑editing (APE) data for nine Indic language pairs, comprising 126,754 instances with multiple aligned labels such as direct assessment, human post‑edit, word‑level OK/BAD tags, and error explanations. The dataset includes a stratified test set across four difficulty axes and supports training and evaluation of six prompted large language models, three COMET metrics, and three APE systems. Experiments reveal that segments with conflicting holistic and token‑level quality signals are consistently ranked lower, while annotator disagreement shows no effect when controlled for score distribution. whyItMatters":"The benchmark provides a unified resource for training and evaluating QE and APE across Indic languages, enabling consistent comparison of models and metrics on a shared dataset."

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