KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines

Alexander Baranov; Anna Palatkina; Makovka Y.; Pavel Braslavski

doi:10.26615/978-954-452-098-4-015

Publications

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KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines

P. 125–132.

Alexander Baranov, Anna Palatkina, Makovka Y., Pavel Braslavski

We present KoWit-24, a dataset with fine-grained annotation of wordplay in 2,700 Russian news headlines. KoWit-24 annotations include the presence of wordplay, its type, wordplay anchors, and words/phrases the wordplay refers to. Unlike the majority of existing humor collections of canned jokes, KoWit-24 provides wordplay contexts – each headline is accompanied by the news lead and summary. The most common type of wordplay in the dataset is the transformation of collocations, idioms, and named entities – the mechanism that has been underrepresented in previous humor datasets. Our experiments with five LLMs show that there is ample room for improvement in wordplay detection and interpretation tasks. The dataset and evaluation scripts are available at https://github.com/Humor-Research/KoWit-24

Language: English

DOI

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Keywords: computational humor

Publication based on the results of:

Complex language and semantic models in artificial intelligence (2025)

In book

Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing

Shumen: INCOMA Ltd, 2025.

You Told Me That Joke Twice: A Systematic Investigation of Transferability and Robustness of Humor Detection Models

Baranov A. M., Kniazhevskii V., Braslavski P., , in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.: Singapore: Association for Computational Linguistics, 2023. P. 13701–13715.

In this study, we focus on automatic humor detection, a highly relevant task for conversational AI. To date, there are several English datasets for this task, but little research on how models trained on them generalize and behave in the wild. To fill this gap, we carefully analyze existing datasets, train RoBERTa-based and Naïve Bayes ...

Added: December 22, 2023

Jokingbird: Funny Headline Generation for News

Login N., Alexander Baranov, Braslavski P., , in: Analysis of Images, Social Networks and Texts. 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021, Revised Selected Papers.: Cham: Springer, 2022. P. 97–109.

In this study, we address the problem of generating funny headlines for news articles. Funny headlines are beneficial even for serious news stories – they attract and entertain the reader. Automatically generated funny headlines can serve as prompts for news editors. More generally, humor generation can be applied to other domains, e.g. conversational systems. Like ...

Added: December 6, 2022

Large Dataset and Language Model Fun-Tuning for Humor Recognition

Blinov V., Bolotova-Baranova V., Braslavski P., , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.: Association for Computational Linguistics, 2019. P. 4027–4032.

The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The ...

Added: October 28, 2019