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iSarcasmEval: Intended Sarcasm Detection

SemEval-2022 Task 6 (iSarcasmEval): detecting intended sarcasm in English and Arabic. Unlike prior datasets labeled by third parties, iSarcasmEval texts are labeled by their own authors for intended sarcasm. Annotators decide whether a text is sarcastic and, if so, which category of ironic speech it expresses.

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Quick start:

bash
git clone https://github.com/davidjurgens/potato-showcase.git
cd potato-showcase/semeval/2022/task06-isarcasmeval
potato start config.yaml

Configuration File

This Potato config reproduces the annotation task. Save it as config.yaml and run potato start config.yaml to try it.

yaml
# iSarcasmEval: Intended Sarcasm Detection
# Based on Abu Farha, Oprea, Wilson, and Magdy, SemEval-2022 Task 6
# Paper: https://aclanthology.org/2022.semeval-1.111/
# Dataset: https://github.com/iabufarha/iSarcasmEval
#
# iSarcasmEval is the first shared task to focus on INTENDED sarcasm: each
# text is labeled by its own author, not by third-party annotators. The
# shared task defines three subtasks:
#   - SubTask A: is the text sarcastic? (binary)
#   - SubTask B: which category of ironic speech does it express?
#   - SubTask C: given a sarcastic text and its non-sarcastic rephrase,
#                tell which one is the sarcastic one (pairwise)
# This showcase models SubTask A (binary) and SubTask B (category), and
# collects an optional non-sarcastic rephrase in the spirit of SubTask C.
#
# SubTask B ironic-speech categories (Leggitt & Gibbs):
# - Sarcasm: contradicts the state of affairs and is critical toward someone
# - Irony: contradicts the state of affairs but is not necessarily critical
# - Satire: ridicules a subject through humor, irony, or exaggeration
# - Understatement: undermines the magnitude of a state of affairs
# - Overstatement: exaggerates the magnitude of a state of affairs
# - Rhetorical Question: a question whose answer is not expected

annotation_task_name: "iSarcasmEval - Intended Sarcasm Detection"
task_dir: "."

data_files:
  - sample-data.json

item_properties:
  id_key: "id"
  text_key: "text"

output_annotation_dir: "annotation_output/"
export_annotation_format: "jsonl"

port: 8000

annotation_schemes:
  - annotation_type: radio
    name: is_sarcastic
    description: "Is this text sarcastic (as intended by its author)?"
    labels:
      - name: "Sarcastic"
        tooltip: "The author intended the text to be sarcastic or ironic"
        key_value: "1"
      - name: "Not Sarcastic"
        tooltip: "The text is meant literally"
        key_value: "2"

  - annotation_type: multiselect
    name: sarcasm_category
    description: "If sarcastic, which category of ironic speech applies? (Select all that apply)"
    labels:
      - name: "Sarcasm"
        tooltip: "Contradicts the state of affairs and is critical toward someone/something"
      - name: "Irony"
        tooltip: "Contradicts the state of affairs but is not necessarily critical"
      - name: "Satire"
        tooltip: "Ridicules a subject through humor, irony, or exaggeration"
      - name: "Understatement"
        tooltip: "Undermines the magnitude of a state of affairs"
      - name: "Overstatement"
        tooltip: "Exaggerates the magnitude of a state of affairs"
      - name: "Rhetorical Question"
        tooltip: "A question posed without expecting an answer"

  - annotation_type: text
    name: rephrase
    description: "If sarcastic, provide a non-sarcastic rephrase that conveys the intended meaning (optional)"
    multiline: true
    required: false
    placeholder: "Non-sarcastic rephrase..."

annotation_instructions: |
  You will see a short social-media text. First decide whether it is
  sarcastic as intended by its author. If it is, select the category or
  categories of ironic speech it expresses, and optionally write a
  non-sarcastic rephrase that conveys the same intended meaning.

user_config:
  allow_all_users: true
max_annotations_per_user: 50
num_annotators_per_item: 3

Sample Data

json
[
  {
    "id": "isc_001",
    "text": "Oh great, another Monday. Just what I was hoping for all weekend."
  },
  {
    "id": "isc_002",
    "text": "The train was delayed by an hour this morning due to signal problems."
  }
]

// ... and 8 more items

Dataset & paper

Abu Farha et al., SemEval 2022

Citation (BibTeX)

bibtex
@inproceedings{abu-farha-etal-2022-semeval,
    title = "{S}em{E}val-2022 Task 6: i{S}arcasm{E}val, Intended Sarcasm Detection in {E}nglish and {A}rabic",
    author = "Abu Farha, Ibrahim  and Oprea, Silviu Vlad  and Wilson, Steven  and Magdy, Walid",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.111",
    doi = "10.18653/v1/2022.semeval-1.111",
    pages = "802--814"
}

Details

Annotation Types

radiomultiselecttext

Domain

NLPSocial MediaSemEval

Use Cases

Sarcasm DetectionFigurative LanguageSentiment Analysis

Tags

semevalsemeval-2022shared-tasksarcasmironyfigurative-language

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