@inproceedings{ruder-etal-2023-xtreme,
title = "{XTREME}-{UP}: A User-Centric Scarce-Data Benchmark for Under-Represented Languages",
author = "Ruder, Sebastian and
Clark, Jonathan and
Gutkin, Alexander and
Kale, Mihir and
Ma, Min and
Nicosia, Massimo and
Rijhwani, Shruti and
Riley, Parker and
Sarr, Jean-Michel and
Wang, Xinyi and
Wieting, John and
Gupta, Nitish and
Katanova, Anna and
Kirov, Christo and
Dickinson, Dana and
Roark, Brian and
Samanta, Bidisha and
Tao, Connie and
Adelani, David and
Axelrod, Vera and
Caswell, Isaac and
Cherry, Colin and
Garrette, Dan and
Ingle, Reeve and
Johnson, Melvin and
Panteleev, Dmitry and
Talukdar, Partha",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.125",
doi = "10.18653/v1/2023.findings-emnlp.125",
pages = "1856--1884",
abstract = "Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) {---} languages for which NLP research is particularly far behind in meeting user needs {---} it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks {---} tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.",
}
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<abstract>Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.</abstract>
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%0 Conference Proceedings
%T XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
%A Ruder, Sebastian
%A Clark, Jonathan
%A Gutkin, Alexander
%A Kale, Mihir
%A Ma, Min
%A Nicosia, Massimo
%A Rijhwani, Shruti
%A Riley, Parker
%A Sarr, Jean-Michel
%A Wang, Xinyi
%A Wieting, John
%A Gupta, Nitish
%A Katanova, Anna
%A Kirov, Christo
%A Dickinson, Dana
%A Roark, Brian
%A Samanta, Bidisha
%A Tao, Connie
%A Adelani, David
%A Axelrod, Vera
%A Caswell, Isaac
%A Cherry, Colin
%A Garrette, Dan
%A Ingle, Reeve
%A Johnson, Melvin
%A Panteleev, Dmitry
%A Talukdar, Partha
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ruder-etal-2023-xtreme
%X Data scarcity is a crucial issue for the development of highly multilingual NLP systems. Yet for many under-represented languages (ULs) — languages for which NLP research is particularly far behind in meeting user needs — it is feasible to annotate small amounts of data. Motivated by this, we propose XTREME-UP, a benchmark defined by: its focus on the scarce-data scenario rather than zero-shot; its focus on user-centric tasks — tasks with broad adoption by speakers of high-resource languages; and its focus on under-represented languages where this scarce-data scenario tends to be most realistic. XTREME-UP evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks that are of general utility. We create new datasets for OCR, autocomplete, semantic parsing, and transliteration, and build on and refine existing datasets for other tasks. XTREME-UP provides methodology for evaluating many modeling scenarios including text only, multi-modal (vision, audio, and text), supervised parameter tuning, and in-context learning. We evaluate commonly used models on the benchmark. We release all code and scripts to train and evaluate models.
%R 10.18653/v1/2023.findings-emnlp.125
%U https://aclanthology.org/2023.findings-emnlp.125
%U https://doi.org/10.18653/v1/2023.findings-emnlp.125
%P 1856-1884
Markdown (Informal)
[XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://aclanthology.org/2023.findings-emnlp.125) (Ruder et al., Findings 2023)
ACL
- Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, et al.. 2023. XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 1856–1884, Singapore. Association for Computational Linguistics.
