mlsanityData to 14 Oct 2026

The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation

arXiv 2311.05552 ↗First posted 9 Nov 2023cs.CLcited by 11

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Abstract (arXiv)

Human evaluation is often considered to be the gold standard method of evaluating a Natural Language Generation system. However, whilst its importance is accepted by the community at large, the quality of its execution is often brought into question. In this position paper, we argue that the generation of more esoteric forms of language - humour, irony and sarcasm - constitutes a subdomain where the characteristics of selected evaluator panels are of utmost importance, and every effort should be made to report demographic characteristics wherever possible, in the interest of transparency and replicability. We support these claims with an overview of each language form and an analysis of examples in terms of how their interpretation is affected by different participant variables. We additionally perform a critical survey of recent works in NLG to assess how well evaluation procedures are reported in this subdomain, and note a severe lack of open reporting of evaluator demographic information, and a significant reliance on crowdsourcing platforms for recruitment.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 28 of its 90 references that are in the map. First posted on arXiv 9 Nov 2023.

Builds on

28 of 90 references.

References · 28

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2021All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Textcited by 287
  3. 2020Text-to-Text Pre-Training for Data-to-Text Taskscited by 104
  4. 2020Content Planning for Neural Story Generation with Aristotelian Rescoringcited by 92
  5. 2022Iteratively Prompt Pre-trained Language Models for Chain of Thoughtcited by 75
  6. 2022Making Science Simple: Corpora for the Lay Summarisation of Scientific Literaturecited by 55
  7. 2021Beyond Fair Pay: Ethical Implications of NLP Crowdsourcingcited by 53
  8. 2022Don't Blame the Annotator: Bias Already Starts in the Annotation Instructionscited by 47
  9. 2019Pun Generation with Surprisecited by 37
  10. 2020A Report on the 2020 Sarcasm Detection Shared Taskcited by 24
  11. 2022AmbiPun: Generating Humorous Puns with Ambiguous Contextcited by 21
  12. 2022ExPUNations: Augmenting Puns with Keywords and Explanationscited by 21
16 more
  1. 2019Pun-GAN: Generative Adversarial Network for Pun Generationcited by 18
  2. 2021Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistantscited by 16
  3. 2023TwistList: Resources and Baselines for Tongue Twister Generationcited by 16
  4. 2020SemEval-2020 Task 7: Assessing Humor in Edited News Headlinescited by 15
  5. 2021A Survey of NLP-Related Crowdsourcing HITs: what works and what does notcited by 15
  6. 2022NGEP: A Graph-based Event Planning Framework for Story Generationcited by 13
  7. 2022A Unified Framework for Pun Generation with Humor Principlescited by 10
  8. 2020"Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Frameworkcited by 8
  9. 2022Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systemscited by 8
  10. 2020Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usagecited by 6
  11. 2022A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectivescited by 6
  12. 2022Assessing Digital Language Support on a Global Scalecited by 6
  13. 2017Punny Captions: Witty Wordplay in Image Descriptionscited by 4
  14. 2020Conservative AI and social inequality: Conceptualizing alternatives to bias through social theorycited by 3
  15. 2021Implicit Gender Bias in Computer Science -- A Qualitative Studycited by 1
  16. 2023"When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruitycited by 1

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Citing papers per year: 2024: 6; 2025: 5.

11 papers.