Showcase/Scientific Claim Verification (SciFact)
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Scientific Claim Verification (SciFact)

Verify scientific claims against evidence from research abstracts. Based on SciFact (Wadden et al., EMNLP 2020). Classify claims as supported, refuted, or having insufficient evidence, and identify rationale sentences.

📝

text annotation

Configuration Fileconfig.yaml

# Scientific Claim Verification (SciFact-style)
# Based on Wadden et al., EMNLP 2020
# Paper: https://aclanthology.org/2020.emnlp-main.609/
# Dataset: https://github.com/allenai/scifact
#
# This task verifies scientific claims against evidence from research abstracts.
# Annotators determine if a claim is SUPPORTED, REFUTED, or has NOT ENOUGH INFO
# based on the provided abstract, and identify which sentences provide evidence.
#
# Verification Labels:
# - SUPPORTS: The abstract provides evidence that the claim is true
# - REFUTES: The abstract provides evidence that the claim is false
# - NOT ENOUGH INFO: The abstract doesn't address the claim conclusively
#
# Annotation Guidelines:
# 1. Read the claim carefully and identify what it's asserting
# 2. Read the full abstract before making a judgment
# 3. Look for sentences that directly address the claim
# 4. SUPPORTS: Evidence confirms the claim (same finding)
# 5. REFUTES: Evidence contradicts the claim (opposite finding)
# 6. NOT ENOUGH INFO: Evidence is tangentially related but doesn't verify/refute
# 7. Mark ALL sentences that provide relevant evidence (rationales)
#
# Important Distinctions:
# - "No effect found" REFUTES claims of an effect
# - Correlation claims differ from causation claims
# - Population-specific findings may not generalize

port: 8000
server_name: localhost
task_name: "Scientific Claim Verification"

data_files:
  - sample-data.json
id_key: id
text_key: abstract

output_file: annotations.json

annotation_schemes:
  # Step 1: Verification label
  - annotation_type: radio
    name: verification
    description: "Based on the abstract, is the CLAIM supported, refuted, or is there not enough information?"
    labels:
      - "SUPPORTS"
      - "REFUTES"
      - "NOT ENOUGH INFO"
    tooltips:
      "SUPPORTS": "The abstract provides evidence that confirms the claim is true"
      "REFUTES": "The abstract provides evidence that contradicts the claim (claim is false)"
      "NOT ENOUGH INFO": "The abstract doesn't contain sufficient information to verify or refute the claim"

  # Step 2: Evidence/rationale identification
  - annotation_type: span
    name: evidence
    description: "Highlight the sentence(s) that provide evidence for your verification decision"
    labels:
      - "Evidence"
    label_colors:
      "Evidence": "#22c55e"
    tooltips:
      "Evidence": "Sentences that directly support or refute the claim"
    allow_overlapping: false

  # Step 3: Confidence
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your verification?"
    min_value: 1
    max_value: 5
    labels:
      1: "Very uncertain"
      2: "Somewhat uncertain"
      3: "Moderately confident"
      4: "Confident"
      5: "Very confident"

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 2
allow_skip: true
skip_reason_required: false

Sample Datasample-data.json

[
  {
    "id": "sci_001",
    "claim": "Vitamin D supplementation reduces the risk of respiratory infections.",
    "abstract": "Background: Vitamin D has immunomodulatory effects that may protect against respiratory infections. Methods: We conducted a meta-analysis of 25 randomized controlled trials including 11,321 participants. Results: Vitamin D supplementation reduced the risk of acute respiratory infection (OR 0.88, 95% CI 0.81-0.96). The protective effect was strongest in participants with baseline vitamin D deficiency. Conclusion: Vitamin D supplementation is safe and provides modest protection against respiratory infections."
  },
  {
    "id": "sci_002",
    "claim": "Coffee consumption increases the risk of cardiovascular disease.",
    "abstract": "Objective: To examine the association between coffee consumption and cardiovascular disease risk. Methods: Prospective cohort study of 468,629 UK Biobank participants followed for 11 years. Results: Compared to non-coffee drinkers, moderate coffee consumption (3-4 cups/day) was associated with lower risk of cardiovascular disease (HR 0.85, 95% CI 0.79-0.92). Heavy consumption (>6 cups/day) showed no significant association. Conclusion: Moderate coffee consumption is associated with reduced cardiovascular risk."
  }
]

// ... and 4 more items

Get This Design

View on GitHub

Clone or download from the repository

Quick start:

git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/scientific-claim-verification
potato start config.yaml

Details

Annotation Types

radiospan

Domain

NLPScientific NLPFact-Checking

Use Cases

Fact VerificationScientific LiteratureMisinformation Detection

Tags

fact-checkingscientificevidencescifactemnlp2020rationale

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