arXiv — NLP / Computation & Language · · 3 min read

ROC Analysis for Evaluating Translation Quality Estimation Systems

Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.

Computer Science > Computation and Language

arXiv:2605.24721 (cs)
[Submitted on 23 May 2026]

Title:ROC Analysis for Evaluating Translation Quality Estimation Systems

Authors:Evelyn Y. Garland (1), Carola F. Berger (2) ((1) Acta-Transphere, (2) CFB Scientific Translations LLC)
View a PDF of the paper titled ROC Analysis for Evaluating Translation Quality Estimation Systems, by Evelyn Y. Garland (1) and Carola F. Berger (2) ((1) Acta-Transphere and 1 other authors
View PDF HTML (experimental)
Abstract:The increasing use of automated translation quality estimation (QE) systems calls for practical, decision-oriented methods for evaluating their performance. We propose that Receiver Operating Characteristic (ROC) analysis is a useful approach for this purpose. Our study shows that ROC analysis not only produces results consistent with currently prevalent methods, but also offers several important advantages, including actionable performance insights that support business decision-making.
Comments: 16 pages, 8 PNG figures, 3 tables, uses this http URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2605.24721 [cs.CL]
  (or arXiv:2605.24721v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.24721
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Evelyn Garland [view email]
[v1] Sat, 23 May 2026 20:23:59 UTC (1,028 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled ROC Analysis for Evaluating Translation Quality Estimation Systems, by Evelyn Y. Garland (1) and Carola F. Berger (2) ((1) Acta-Transphere and 1 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source

Current browse context:

cs.CL
< prev   |   next >
Change to browse by:
cs

References & Citations

Loading...

BibTeX formatted citation

loading...
Data provided by:

Bookmark

BibSonomy Reddit
Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos

Demos

Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers

Recommenders and Search Tools

Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Discussion (0)

Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.

Sign in →

No comments yet. Sign in and be the first to say something.

More from arXiv — NLP / Computation & Language