@inproceedings{kanojia-etal-2026-indicqe,
title = "{I}ndic{QE}-{APE}: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing over {I}ndic Languages",
author = "Kanojia, Diptesh and
Sindhujan, Archchana and
Deoghare, Sourabh and
Sokova, Daria and
Qian, Shenbin and
Koushik, Girish and
Ranasinghe, Tharindu and
Orasan, Constantin and
Zerva, Chrysoula and
Rei, Ricardo and
Blain, Frederic and
Martins, Andr{\'e} and
Turchi, Marco and
Negri, Matteo and
Kunchukuttan, Anoop and
Khapra, Mitesh M. and
Bhattacharyya, Pushpak",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.19/",
pages = "318--344",
ISBN = "979-8-89176-474-3",
abstract = "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-APE: 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 tags and an error explanation, and a test set stratified over four difficulty axes. We benchmark six prompted LLMs and three COMET metrics on segment-level QE, and three systems on APE. Two of the axes are defined partly on direct assessment and select a compressed slice of it. Segments whose segment-level and token-level signals disagree are ranked below equally scored segments of the same language. Four-shot prompting costs every model at or below 3.4B both correlation and output-format compliance. Unedited MT beats every APE system we run on three of the four pairs. The benchmark and code are released."
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<abstract>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-APE: 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 tags and an error explanation, and a test set stratified over four difficulty axes. We benchmark six prompted LLMs and three COMET metrics on segment-level QE, and three systems on APE. Two of the axes are defined partly on direct assessment and select a compressed slice of it. Segments whose segment-level and token-level signals disagree are ranked below equally scored segments of the same language. Four-shot prompting costs every model at or below 3.4B both correlation and output-format compliance. Unedited MT beats every APE system we run on three of the four pairs. The benchmark and code are released.</abstract>
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%0 Conference Proceedings
%T IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing over Indic Languages
%A Kanojia, Diptesh
%A Sindhujan, Archchana
%A Deoghare, Sourabh
%A Sokova, Daria
%A Qian, Shenbin
%A Koushik, Girish
%A Ranasinghe, Tharindu
%A Orasan, Constantin
%A Zerva, Chrysoula
%A Rei, Ricardo
%A Blain, Frederic
%A Martins, André
%A Turchi, Marco
%A Negri, Matteo
%A Kunchukuttan, Anoop
%A Khapra, Mitesh M.
%A Bhattacharyya, Pushpak
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F kanojia-etal-2026-indicqe
%X 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-APE: 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 tags and an error explanation, and a test set stratified over four difficulty axes. We benchmark six prompted LLMs and three COMET metrics on segment-level QE, and three systems on APE. Two of the axes are defined partly on direct assessment and select a compressed slice of it. Segments whose segment-level and token-level signals disagree are ranked below equally scored segments of the same language. Four-shot prompting costs every model at or below 3.4B both correlation and output-format compliance. Unedited MT beats every APE system we run on three of the four pairs. The benchmark and code are released.
%U https://aclanthology.org/2026.wmt-main.19/
%P 318-344
Markdown (Informal)
[IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing over Indic Languages](https://aclanthology.org/2026.wmt-main.19/) (Kanojia et al., WMT 2026)
ACL
- Diptesh Kanojia, Archchana Sindhujan, Sourabh Deoghare, Daria Sokova, Shenbin Qian, Girish Koushik, Tharindu Ranasinghe, Constantin Orasan, Chrysoula Zerva, Ricardo Rei, Frederic Blain, André Martins, Marco Turchi, Matteo Negri, Anoop Kunchukuttan, Mitesh M. Khapra, and Pushpak Bhattacharyya. 2026. IndicQE-APE: A Consolidated Benchmark for Quality Estimation and Automatic Post-Editing over Indic Languages. In Proceedings of the Eleventh Conference on Machine Translation, pages 318–344, Budapest, Hungary. Association for Computational Linguistics.