HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for
            Humor Recognition

Donahue, David M.; Romanov, Alexey; Rumshisky, Anna

doi:10.18653/v1/s17-2010

Cited by 8 publications

(9 citation statements)

References 10 publications

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“…This is in opposition to other systems that relied on the output of separate tools, or looking up terms in corpora. Some teams, such as HumorHawk 8 (Donahue et al, 2017) and #WarTeam, used a combination of these two types of systems, and notably, the system that was ranked first in Subtask A (HumorHawk) was an ensemble system that utilized prediction from both feature-based and neural networks-based models.…”

Section: System Analysismentioning

confidence: 99%

SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor

Potash¹,

Romanov²,

Rumshisky³

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the 'Hashtag Wars' segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872.

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Section: System Analysismentioning

confidence: 99%

SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor

Potash¹,

Romanov²,

Rumshisky³

2017

Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Self Cite

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“…Humor detection is usually formulated as a binary text classification problem. Example domains include knock-knock jokes (Taylor and Mazlack, 2004), one-liners (Miller et al, 2017;Simpson et al, 2019;Liu et al, 2018;Mihalcea and Strapparava, 2005;Blinov et al, 2019;Mihalcea and Strapparava, 2006), humorous tweets (Maronikolakis et al, 2020;Donahue et al, 2017;Ortega-Bueno et al, 2018;Zhang and Liu, 2014), humorous product reviews (Ziser et al, 2020;, TV sitcoms (Bertero and Fung, 2016), short stories (Wilmot and Keller, 2020), cartoons captions (Shahaf et al, 2015), and even "That's what she said" jokes (Hossain et al, 2017;Kiddon and Brun, 2011). Related tasks such as irony, sarcasm and satire have also been explored in similarly narrow domains (Davidov et al, 2010;Ptáček et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

Shani¹,

Borenstein²,

Shahaf³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem.In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?"). This challenging task has unique characteristics that make it particularly suitable for automatic learning.We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.

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“…Earlier work, like (Donahue et al, 2017), use recurrent deep learning methods with dense embeddings to predict humorous tweets. In order to factor both meaning and sound in their analysis, they use GloVe embeddings combined with a novel phonetic representation as input to an LSTM.…”

Section: Related Workmentioning

confidence: 99%

Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense

Zylich¹,

Gugnani²,

Brookman³

et al. 2021

Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

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This paper describes our submission to the SemEval'21: Task 7-HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as on other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1b with an RMSE of 0.5339.

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HumorHawk at SemEval-2017 Task 6: Mixing Meaning and Sound for Humor Recognition

Cited by 8 publications

References 10 publications

SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor

SemEval-2017 Task 6: #HashtagWars: Learning a Sense of Humor

How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense

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