Skip to content
OpenTrain AIFor AI Companies
← Back to explorer

Introducing corpora Hlava Cor and Hlava AD: Human Label Variation in Coreference and Discourse Relations

Anna Nedoluzhko, Šárka Zikánová, Jiří Mírovský, Milan Straka, Eva Hajičová · Jun 24, 2026 · Citations: 0

How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the exact study setup in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another. To explore this phenomenon, we created two corpora with multiple annotations of Czech texts, accompanied by annotators' explanations of their choices. The first corpus consists of 1,024 contexts annotated in parallel by three annotators. It captures differences in the identification of coreference across various text types and grammatical-semantic categories, including pronouns, full noun phrases, and anaphoric adverbials. The second corpus comprises 512 contexts, annotated in parallel by five annotators, and focuses on identifying discourse relations in attributive and non-attributive constructions. Both corpora achieve a comparable inter-annotator agreement of approximately 60-65%. For coreference annotation, agreement tends to be lower in cases where automatic coreference resolution models disagree, suggesting that when the models disagree, the examples tend to be more difficult or ambiguous for human annotators to interpret. The annotators' comments, both for coreference and discourse relations, further reveal differences in interpretation, varying levels of confidence in text understanding, and individual reading strategies.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.

Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

15/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 45%

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another."

Quality Controls

partial

Inter Annotator Agreement Reported

Calibration/adjudication style controls detected.

"As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another."

Reported Metrics

partial

Agreement, Coherence

Useful for evaluation criteria comparison.

"As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Inter Annotator Agreement Reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

agreementcoherence

Research Brief

Metadata summary

As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another.
  • To explore this phenomenon, we created two corpora with multiple annotations of Czech texts, accompanied by annotators' explanations of their choices.
  • The first corpus consists of 1,024 contexts annotated in parallel by three annotators.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another.
  • To explore this phenomenon, we created two corpora with multiple annotations of Czech texts, accompanied by annotators' explanations of their choices.
  • Both corpora achieve a comparable inter-annotator agreement of approximately 60-65%.

Why It Matters For Eval

  • As previous research on annotator disagreement in discourse phenomena has shown, understanding text coherence varies considerably from one individual to another.
  • Both corpora achieve a comparable inter-annotator agreement of approximately 60-65%.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Pass: Quality control reporting appears

    Detected: Inter Annotator Agreement Reported

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: agreement, coherence

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.