XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented
  Languages
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 re-search 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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Sebastian Ruder
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Jonathan H. Clark
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Alexander Gutkin
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Mihir Kale
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Min Ma
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Massimo Nicosia
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2023-05-24T07:51:33.903Z
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Massimo Nicosia
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MassimoNicosia
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Shruti Rijhwani
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Parker Riley
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Jean-Michel A. Sarr
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Xinyi Wang
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John Wieting
646c1c67e96a751c5255af12
Nitish Gupta
646c1c67e96a751c5255af13
Anna Katanova
646c1c67e96a751c5255af14
Christo Kirov
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Dana L. Dickinson
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Brian Roark
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Bidisha Samanta
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Connie Tao
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David I. Adelani
claimed_verified
2023-05-23T12:22:37.722Z
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David Adelani
user
Davlan
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Vera Axelrod
claimed_verified
2023-05-26T08:07:31.517Z
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Vera Axelrod
user
vaxelrod
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Isaac Caswell
extracted_pending
2023-05-23T01:52:41.124Z
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Isaac Caswell
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icaswell
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Colin Cherry
claimed_verified
2023-05-24T07:51:31.279Z
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Colin Cherry
user
ColinCherry
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Dan Garrette
646c1c67e96a751c5255af1e
Reeve Ingle
646c1c67e96a751c5255af1f
Melvin Johnson
646c1c67e96a751c5255af20
Dmitry Panteleev
646c1c67e96a751c5255af21
Partha Talukdar
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented
  Languages
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 re-search 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
XTREME-UP is a benchmark evaluating language models across 88 under-represented languages on user-centric tasks with limited data, focusing on ASR, OCR, MT, and information access technologies.
ASR OCR MT information access tasks text-only multi-modal parameter tuning in-context learning