@inproceedings{park-etal-2024-memeintent,
title = "{M}eme{I}ntent: Benchmarking Intent Description Generation for Memes",
author = "Park, Jeongsik and
Nguyen, Khoi P. N. and
Li, Terrence and
Shrestha, Suyesh and
Vu, Megan Kim and
Wang, Jerry Yining and
Ng, Vincent",
editor = "Kawahara, Tatsuya and
Demberg, Vera and
Ultes, Stefan and
Inoue, Koji and
Mehri, Shikib and
Howcroft, David and
Komatani, Kazunori",
booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2024",
address = "Kyoto, Japan",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigdial-1.54",
pages = "631--643",
abstract = "While recent years have seen a surge of interest in the automatic processing of memes, much of the work in this area has focused on determining whether a meme contains malicious content. This paper proposes the new task of \textit{intent description generation}: generating a description of the author{'}s intentions when creating the meme. To stimulate future work on this task, we (1) annotated a corpus of memes with the intents being perceived by the reader as well as the background knowledge needed to infer the intents and (2) established baseline performance on the intent description generation task using state-of-the-art large language models. Our results suggest the importance of background knowledge retrieval in intent description generation for memes.",
}
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<abstract>While recent years have seen a surge of interest in the automatic processing of memes, much of the work in this area has focused on determining whether a meme contains malicious content. This paper proposes the new task of intent description generation: generating a description of the author’s intentions when creating the meme. To stimulate future work on this task, we (1) annotated a corpus of memes with the intents being perceived by the reader as well as the background knowledge needed to infer the intents and (2) established baseline performance on the intent description generation task using state-of-the-art large language models. Our results suggest the importance of background knowledge retrieval in intent description generation for memes.</abstract>
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%0 Conference Proceedings
%T MemeIntent: Benchmarking Intent Description Generation for Memes
%A Park, Jeongsik
%A Nguyen, Khoi P. N.
%A Li, Terrence
%A Shrestha, Suyesh
%A Vu, Megan Kim
%A Wang, Jerry Yining
%A Ng, Vincent
%Y Kawahara, Tatsuya
%Y Demberg, Vera
%Y Ultes, Stefan
%Y Inoue, Koji
%Y Mehri, Shikib
%Y Howcroft, David
%Y Komatani, Kazunori
%S Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2024
%8 September
%I Association for Computational Linguistics
%C Kyoto, Japan
%F park-etal-2024-memeintent
%X While recent years have seen a surge of interest in the automatic processing of memes, much of the work in this area has focused on determining whether a meme contains malicious content. This paper proposes the new task of intent description generation: generating a description of the author’s intentions when creating the meme. To stimulate future work on this task, we (1) annotated a corpus of memes with the intents being perceived by the reader as well as the background knowledge needed to infer the intents and (2) established baseline performance on the intent description generation task using state-of-the-art large language models. Our results suggest the importance of background knowledge retrieval in intent description generation for memes.
%U https://aclanthology.org/2024.sigdial-1.54
%P 631-643
Markdown (Informal)
[MemeIntent: Benchmarking Intent Description Generation for Memes](https://aclanthology.org/2024.sigdial-1.54) (Park et al., SIGDIAL 2024)
ACL
- Jeongsik Park, Khoi P. N. Nguyen, Terrence Li, Suyesh Shrestha, Megan Kim Vu, Jerry Yining Wang, and Vincent Ng. 2024. MemeIntent: Benchmarking Intent Description Generation for Memes. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 631–643, Kyoto, Japan. Association for Computational Linguistics.