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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."
Indic quality estimation (QE) and automatic post-editing (APE) data is spread across separate releases, so no single resource supports training and evaluation across tasks and language pairs on one footing. We consolidate the WMT 2020--2024 shared-task lineage with an extended English--Malayalam resource into \indicqe: $126{,}754$ instances over nine directional pairs, with up to four label types aligned on the same segment, a direct assessment, a human post-edit, word-level OK/BAD tags and an error explanation, and a test set stratified over four difficulty axes.
The paper introduces a black‑box, inference‑time diagnostic for low‑resource Automatic Post‑Editing (APE) that distinguishes whether poor performance is due to insufficient training data or inconsistent training signals. By varying an edit‑distance penalty and analyzing the resulting TER‑vs‑λ curve and confidence‑based constraint ordering, the authors identify two failure modes—Binary Collapse and Confident Miscalibration—across multiple language pairs. The diagnostic also suggests practical next steps, such as applying a static constraint for immediate accuracy gains, and the authors release new English‑Sinhala and English‑Tamil APE datasets with accompanying code.
COMET reports translation quality as a single number, and that number is routinely compared across target languages written in different scripts. Such a comparison assumes Script Invariance: the score...
arXiv:2610.08159v1 Announce Type: cross Abstract: COMET reports translation quality as a single number, and that number is routinely compared across target languages written in different scripts. Suc...
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