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Evaluating Crowdsourcing Participants in the Absence of Ground-Truth

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Ramanathan Subramanian, Romer Rosales, Glenn Fung, Jennifer Dy

Human-Computer Interaction · Submitted 30 May 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.

how it is stated · asserted 0.63 read the record →

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arXiv ID1605.09432 (version 1)
Pith ID67QLEMHZ, Pith's permanent record of this paper and its review
CitationGlenn Fung, Jennifer Dy, Ramanathan Subramanian, Romer Rosales. "Evaluating Crowdsourcing Participants in the Absence of Ground-Truth." arXiv:1605.09432 (2016). https://pith.science/paper/67QLEMHZ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 1,618 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.12 · human participants 0.80 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.02 · compares with prior results 0.07 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators. background 0.82 0.17 0.07 asserted 0.63

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from argument 0.53.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Given a supervised/semi-supervised learning scenario where multiple annotators are available, we consider the problem of identification of adversarial or unreliable annotators.

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