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researcher

J. Boyd-Graber

3 papers hereh-index 4258 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author2

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CL3
same name
  • J. Boyd-Graber — 23 papers, h 15
  • J. Boyd-Graber — 23 papers, h 9
  • J. Boyd-Graber — 16 papers, h 22
  • J. Boyd-Graber — 2 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202023
most citedCold-start Active Learning through Self-supervised Language Modeling

6 citations · 7 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CL2023★ 1 cited

MegaWika: Millions of reports and their sources across 50 diverse languages

Samuel Barham, Orion Weller, Michelle Yuan +9

To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…

cs.CL2021

Adapting Coreference Resolution Models through Active Learning

Michelle Yuan, Patrick Xia, Chandler May +2

Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data…

cs.CL2020★ 6 cited

Cold-start Active Learning through Self-supervised Language Modeling

Michelle Yuan, Hsuan-Tien Lin, Jordan Boyd-Graber

Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification mode…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.