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."
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
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.
By Isuru Wijesiri, Nisansa de Silva, Kavindu Warnakulasuriya, Aloka Fernando, Surangika Ranathunga
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...
By G. L. John Salvin (Indian Institute of Technology Palakkad), Swapnil Hingmire (Indian Institute of Technology Palakkad)
arXiv:2507.23248v2 Announce Type: replace-cross
Abstract: Bengali is spoken by more than 230 million people, yet no standardized instrument evaluates large language models (LLMs) on Bengali across th...
By Shimanto Bhowmik, Tawsif Tashwar Dipto, Md Sazzad Islam, Sheryl Hsu, Tahsin Reasat
arXiv:2609.16340v1 Announce Type: cross
Abstract: Machine translation systems are periodically upgraded to stronger models, but the available preference signal is human post-edits of an older system'...
By Rohit Dhaipule, Sukhdeep Singh Kharbanda, Prasanth Bathala, Pradyumna Lanka, Anubhav Shrimal
arXiv:2609.23417v1 Announce Type: cross
Abstract: Screen-recorded web editing requests contain weak deictic expressions such as ``this'' and ``there,'' whose referents depend on speech, cursor trajec...
By Minghao Han, Zhenghao Xing, Xize Cheng, Yuxuan Wang, Junming Lin, Ling Wang, Yinsong Yan, Yunfei Chu, Qize Yang, Jin Xu
SyntaxBench is a diagnostic benchmark and statistical evaluation framework for character‑level reasoning in large language models, comprising five core tasks—character counting, letter containment, palindrome detection, edit distance, and longest‑string selection—and a harder substring‑extraction stress test called index_to_span. The benchmark uses paired English and random‑string inputs, zero‑, one‑, and four‑shot prompts, and evaluates models from 2B to 32B parameters across multiple reasoning modes. It reports a wide range of metrics, including exact‑match and relaxed accuracy, Cohen’s kappa, McNemar tests, bootstrap confidence intervals, Kendall’s tau, class‑conditional metrics, tokenization analysis, and multiple‑comparison‑corrected tests.
By Mohsen Larni (Department of Computer Science, University of Nevada, Las Vegas), Sobhan Ebrahimi Azar (Department of Computer Science, University of Nevada, Las Vegas), Pouyan Nahed (Department of Computer Science, University of Nevada, Las Vegas), Kazem Taghva (Department of Computer Science, University of Nevada, Las Vegas)
arXiv:2610.07545v1 Announce Type: new
Abstract: We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retra...
By Zubair Ajmal Farooq, Diptesh Kanojia
arXiv:2610.18420v1 Announce Type: cross
Abstract: We trained Agaram 2.5B, a 2.5-billion-parameter Tamil-English language model, from scratch on about 50 billion tokens (58% Tamil, the rest English, m...
By Ramchand Kumaresan, Madhusudhanan G, Kaushik Kezendirane
arXiv:2610.15371v1 Announce Type: cross
Abstract: Gnani-Evon-v3.3-30B-A3B is a long-context hybrid Mamba-Transformer mixture-of-experts model for reasoning, instruction following, and tool use in Eng...
By Sutanshu Saksena Raj, Prasanjith Pasunuti, Parijat Chatterjee, Daksh Maan, Purusharth Malik, Nayanjit Sarkar, Avinash Benki