NLI with Explanations (e-SNLI)

# NLI with Explanations (e-SNLI)
# Based on Camburu et al., NeurIPS 2018
# Paper: https://papers.nips.cc/paper/8163-e-snli
# Dataset: https://github.com/OanaMariaCamburu/e-SNLI
#
# Natural Language Inference (NLI) determines the relationship between
# a premise and hypothesis. e-SNLI adds human-written explanations.
#
# NLI Labels:
# - Entailment: The hypothesis is definitely TRUE given the premise
# - Contradiction: The hypothesis is definitely FALSE given the premise
# - Neutral: The hypothesis may or may not be true (can't determine)
#
# Explanation Guidelines:
# 1. First classify the relationship
# 2. Then highlight key words in premise and hypothesis
# 3. Write a natural language explanation justifying your label
# 4. Focus on NON-OBVIOUS elements that determine the relation
# 5. Don't just repeat what's identical in both sentences
#
# For Entailment: Explain why hypothesis must be true
# For Contradiction: Identify the conflicting information
# For Neutral: Explain what information is missing/uncertain
#
# Good explanations are:
# - Concise but complete
# - Reference specific words/phrases
# - Explain the reasoning, not just restate

annotation_task_name: "NLI with Explanations"
task_dir: "."

data_files:
  - sample-data.json
item_properties:
  id_key: "id"
  text_key: "pair"

output_annotation_dir: "annotation_output/"
output_annotation_format: "json"

annotation_schemes:
  # Step 1: NLI label
  - annotation_type: radio
    name: label
    description: "Given the PREMISE, what is the relationship with the HYPOTHESIS?"
    labels:
      - "Entailment"
      - "Contradiction"
      - "Neutral"
    tooltips:
      "Entailment": "If the premise is true, the hypothesis MUST be true"
      "Contradiction": "If the premise is true, the hypothesis MUST be false"
      "Neutral": "The premise doesn't determine whether the hypothesis is true or false"

  # Step 2: Highlight key words in premise
  - annotation_type: span
    name: premise_highlights
    description: "Highlight the words in the PREMISE that are most relevant to your decision"
    labels:
      - "Key Evidence"
    label_colors:
      "Key Evidence": "#3b82f6"
    tooltips:
      "Key Evidence": "Words or phrases that are crucial for determining the relationship"
    allow_overlapping: false

  # Step 3: Highlight key words in hypothesis
  - annotation_type: span
    name: hypothesis_highlights
    description: "Highlight the words in the HYPOTHESIS that are most relevant to your decision"
    labels:
      - "Key Evidence"
    label_colors:
      "Key Evidence": "#22c55e"
    tooltips:
      "Key Evidence": "Words or phrases that are crucial for determining the relationship"
    allow_overlapping: false

  # Step 4: Natural language explanation (the defining e-SNLI output)
  - annotation_type: text
    name: explanation
    description: "Write a natural language explanation of why the label holds, using the words you highlighted. Do not simply restate what is identical in both sentences."

  # Step 5: Confidence
  - annotation_type: likert
    name: confidence
    description: "How confident are you in your classification?"
    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: 100
annotation_per_instance: 3
allow_skip: true
skip_reason_required: false