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Marzena Karpinska

University of Massachusetts Amherst

4 papers hereh-index 161.6k citations34 works total

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

author position
  • first author2
  • middle author2

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL4
affiliations
  • University of Massachusetts Amherst
HomepageORCID 0000-0001-6912-0607
same name
  • Marzena Karpinska — 1 paper

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

most citedezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

4 citations · 9 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2022★ 3 cited

Exploring Document-Level Literary Machine Translation with Parallel Paragraphs from World Literature

Katherine Thai, Marzena Karpinska, Kalpesh Krishna +4

Literary translation is a culturally significant task, but it is bottlenecked by the small number of qualified literary translators relative to the many untranslated works publishe…

cs.CL2022★ 2 cited

DEMETR: Diagnosing Evaluation Metrics for Translation

Marzena Karpinska, Nishant Raj, Katherine Thai +3

While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particula…

cs.CL2022★ 4 cited

ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution

Ankita Gupta, Marzena Karpinska, Wenlong Zhao +5

Large-scale, high-quality corpora are critical for advancing research in coreference resolution. However, existing datasets vary in their definition of coreferences and have been c…

cs.CL2021

The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation

Marzena Karpinska, Nader Akoury, Mohit Iyyer

Recent text generation research has increasingly focused on open-ended domains such as story and poetry generation. Because models built for such tasks are difficult to evaluate au…

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