@inproceedings{sun-etal-2022-expunations,
title = "{E}x{PUN}ations: Augmenting Puns with Keywords and Explanations",
author = "Sun, Jiao and
Narayan-Chen, Anjali and
Oraby, Shereen and
Cervone, Alessandra and
Chung, Tagyoung and
Huang, Jing and
Liu, Yang and
Peng, Nanyun",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.304/",
doi = "10.18653/v1/2022.emnlp-main.304",
pages = "4590--4605",
abstract = "The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation, to challenge the current state-of-the-art natural language understanding and generation models' ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers."
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<abstract>The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation, to challenge the current state-of-the-art natural language understanding and generation models’ ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers.</abstract>
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%0 Conference Proceedings
%T ExPUNations: Augmenting Puns with Keywords and Explanations
%A Sun, Jiao
%A Narayan-Chen, Anjali
%A Oraby, Shereen
%A Cervone, Alessandra
%A Chung, Tagyoung
%A Huang, Jing
%A Liu, Yang
%A Peng, Nanyun
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sun-etal-2022-expunations
%X The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master. Puns, in particular, add the challenge of fusing that knowledge with the ability to interpret lexical-semantic ambiguity. In this paper, we present the ExPUNations (ExPUN) dataset, in which we augment an existing dataset of puns with detailed crowdsourced annotations of keywords denoting the most distinctive words that make the text funny, pun explanations describing why the text is funny, and fine-grained funniness ratings. This is the first humor dataset with such extensive and fine-grained annotations specifically for puns. Based on these annotations, we propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation, to challenge the current state-of-the-art natural language understanding and generation models’ ability to understand and generate humor. We showcase that the annotated keywords we collect are helpful for generating better novel humorous texts in human evaluation, and that our natural language explanations can be leveraged to improve both the accuracy and robustness of humor classifiers.
%R 10.18653/v1/2022.emnlp-main.304
%U https://aclanthology.org/2022.emnlp-main.304/
%U https://doi.org/10.18653/v1/2022.emnlp-main.304
%P 4590-4605
Markdown (Informal)
[ExPUNations: Augmenting Puns with Keywords and Explanations](https://aclanthology.org/2022.emnlp-main.304/) (Sun et al., EMNLP 2022)
ACL
- Jiao Sun, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Tagyoung Chung, Jing Huang, Yang Liu, and Nanyun Peng. 2022. ExPUNations: Augmenting Puns with Keywords and Explanations. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4590–4605, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.