Commonsense QA Explanation (ECQA)

# Commonsense QA Explanation (ECQA)
# Based on Aggarwal et al., ACL 2021
# Paper: https://aclanthology.org/2021.acl-long.238/
# Dataset: https://github.com/dair-iitd/ECQA-Dataset
#
# ECQA provides explanations for CommonsenseQA that:
# 1. Justify why the correct answer is right (positive properties)
# 2. Explain why incorrect answers are wrong (negative properties)
# 3. Combine into a free-flow explanation
#
# Explanation Structure:
# - Positives: Facts that support the correct answer
# - Negatives: Facts that refute the incorrect answers
# - Full explanation: Natural language justification
#
# Annotation Guidelines:
# 1. First identify what makes the correct answer correct
# 2. Then identify why each wrong answer fails
# 3. Use commonsense knowledge, not just word matching
# 4. Explanations should be clear to someone unfamiliar with the question
# 5. Focus on the KEY distinguishing properties

port: 8000
server_name: localhost
task_name: "Commonsense QA Explanation"

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

output_file: annotations.json

annotation_schemes:
  # Step 1: Verify the correct answer
  - annotation_type: radio
    name: answer_verification
    description: "Is the marked answer correct?"
    labels:
      - "Yes, correct"
      - "No, incorrect"
      - "Ambiguous"
    tooltips:
      "Yes, correct": "The marked answer is definitely correct"
      "No, incorrect": "The marked answer is wrong"
      "Ambiguous": "Multiple answers could be correct"

  # Step 2: Explanation completeness
  - annotation_type: radio
    name: explanation_quality
    description: "How complete is a good explanation for this question?"
    labels:
      - "Simple - one fact needed"
      - "Moderate - few facts needed"
      - "Complex - many facts needed"
    tooltips:
      "Simple - one fact needed": "One commonsense fact explains the answer"
      "Moderate - few facts needed": "2-3 facts are needed"
      "Complex - many facts needed": "Requires combining multiple pieces of knowledge"

  # Step 3: Knowledge type required
  - annotation_type: radio
    name: knowledge_type
    description: "What type of knowledge is primarily needed to answer this?"
    labels:
      - "Physical/spatial"
      - "Social/cultural"
      - "Temporal"
      - "Causal"
      - "Definitional"
      - "Other"
    tooltips:
      "Physical/spatial": "Knowledge about physical properties or locations"
      "Social/cultural": "Knowledge about social norms or cultural practices"
      "Temporal": "Knowledge about time, sequences, or duration"
      "Causal": "Knowledge about cause and effect"
      "Definitional": "Knowledge about what things are or mean"
      "Other": "Other type of commonsense knowledge"

  # Step 4: Difficulty
  - annotation_type: likert
    name: difficulty
    description: "How difficult is this question for most people?"
    min_value: 1
    max_value: 5
    labels:
      1: "Very easy"
      2: "Easy"
      3: "Moderate"
      4: "Hard"
      5: "Very hard"

allow_all_users: true
instances_per_annotator: 50
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false