title: XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented
    Languages
summary: 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
paper_authors._id: 646c1c67e96a751c5255af07
paper_authors.name: Sebastian Ruder
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paper_authors.name: Jonathan H. Clark
paper_authors._id: 646c1c67e96a751c5255af09
paper_authors.name: Alexander Gutkin
paper_authors._id: 646c1c67e96a751c5255af0a
paper_authors.name: Mihir Kale
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paper_authors.name: Min Ma
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paper_authors.name: Massimo Nicosia
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paper_authors.fullname: Massimo Nicosia
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paper_authors.user: MassimoNicosia
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paper_authors.name: Shruti Rijhwani
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paper_authors.name: Parker Riley
paper_authors._id: 646c1c67e96a751c5255af0f
paper_authors.name: Jean-Michel A. Sarr
paper_authors._id: 646c1c67e96a751c5255af10
paper_authors.name: Xinyi Wang
paper_authors._id: 646c1c67e96a751c5255af11
paper_authors.name: John Wieting
paper_authors._id: 646c1c67e96a751c5255af12
paper_authors.name: Nitish Gupta
paper_authors._id: 646c1c67e96a751c5255af13
paper_authors.name: Anna Katanova
paper_authors._id: 646c1c67e96a751c5255af14
paper_authors.name: Christo Kirov
paper_authors._id: 646c1c67e96a751c5255af15
paper_authors.name: Dana L. Dickinson
paper_authors._id: 646c1c67e96a751c5255af16
paper_authors.name: Brian Roark
paper_authors._id: 646c1c67e96a751c5255af17
paper_authors.name: Bidisha Samanta
paper_authors._id: 646c1c67e96a751c5255af18
paper_authors.name: Connie Tao
paper_authors._id: 646c1c67e96a751c5255af19
paper_authors.name: David I. Adelani
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paper_authors.fullname: David Adelani
paper_authors.type: user
paper_authors.user: Davlan
paper_authors._id: 646c1c67e96a751c5255af1a
paper_authors.name: Vera Axelrod
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/4257b1f01d9aec00f1441027206e8335.svg
paper_authors.fullname: Vera Axelrod
paper_authors.type: user
paper_authors.user: vaxelrod
paper_authors._id: 646c1c67e96a751c5255af1b
paper_authors.name: Isaac Caswell
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2023-05-23T01:52:41.124Z
paper_authors._id: 617c7165e1dfc7bbb3375abe
paper_authors.avatarUrl: /avatars/f628fb21c35eaaec5ff9519c82848c44.svg
paper_authors.fullname: Isaac Caswell
paper_authors.type: user
paper_authors.user: icaswell
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paper_authors.name: Colin Cherry
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-24T07:51:31.279Z
paper_authors._id: 6144bb22207b8aba44e8ec59
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1631896530912-6144bb22207b8aba44e8ec59.jpeg
paper_authors.fullname: Colin Cherry
paper_authors.type: user
paper_authors.user: ColinCherry
paper_authors._id: 646c1c67e96a751c5255af1d
paper_authors.name: Dan Garrette
paper_authors._id: 646c1c67e96a751c5255af1e
paper_authors.name: Reeve Ingle
paper_authors._id: 646c1c67e96a751c5255af1f
paper_authors.name: Melvin Johnson
paper_authors._id: 646c1c67e96a751c5255af20
paper_authors.name: Dmitry Panteleev
paper_authors._id: 646c1c67e96a751c5255af21
paper_authors.name: Partha Talukdar
paper_title: XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented
    Languages
paper_summary: 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
paper_ai_keywords: ASR OCR MT information access tasks