A Semantic Enhancement Framework for Multimodal Sarcasm Detection

Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current ap...
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Autor*in:	Weiyu Zhong [verfasserIn] Zhengxuan Zhang [verfasserIn] Qiaofeng Wu [verfasserIn] Yun Xue [verfasserIn] Qianhua Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	multimodal sarcasm detection contrastive learning graph neural networks social media

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 12(2024), 2, p 317
Übergeordnetes Werk:	volume:12 ; year:2024 ; number:2, p 317

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math12020317

Katalog-ID:	DOAJ096321067

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source	In Mathematics 12(2024), 2, p 317 volume:12 year:2024 number:2, p 317
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callnumber-first	Q - Science
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topic	misc QA1-939 misc multimodal sarcasm detection misc contrastive learning misc graph neural networks misc social media misc Mathematics
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title	A Semantic Enhancement Framework for Multimodal Sarcasm Detection
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abstract	Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus on modeling text–image incongruity at the token level and use the incongruity as the key to detection, ignoring the significance of the overall multimodal features and textual semantics during processing. Moreover, semantic information from other samples with a similar manner of expression also facilitates sarcasm detection. In this work, a semantic enhancement framework is proposed to address image–text congruity by modeling textual and visual information at the multi-scale and multi-span token level. The efficacy of textual semantics in multimodal sarcasm detection is pronounced. Aiming to bridge the cross-modal semantic gap, semantic enhancement is performed by using a multiple contrastive learning strategy. Experiments were conducted on a benchmark dataset. Our model outperforms the latest baseline by 1.87% in terms of the F1-score and 1% in terms of accuracy.
abstractGer	Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus on modeling text–image incongruity at the token level and use the incongruity as the key to detection, ignoring the significance of the overall multimodal features and textual semantics during processing. Moreover, semantic information from other samples with a similar manner of expression also facilitates sarcasm detection. In this work, a semantic enhancement framework is proposed to address image–text congruity by modeling textual and visual information at the multi-scale and multi-span token level. The efficacy of textual semantics in multimodal sarcasm detection is pronounced. Aiming to bridge the cross-modal semantic gap, semantic enhancement is performed by using a multiple contrastive learning strategy. Experiments were conducted on a benchmark dataset. Our model outperforms the latest baseline by 1.87% in terms of the F1-score and 1% in terms of accuracy.
abstract_unstemmed	Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus on modeling text–image incongruity at the token level and use the incongruity as the key to detection, ignoring the significance of the overall multimodal features and textual semantics during processing. Moreover, semantic information from other samples with a similar manner of expression also facilitates sarcasm detection. In this work, a semantic enhancement framework is proposed to address image–text congruity by modeling textual and visual information at the multi-scale and multi-span token level. The efficacy of textual semantics in multimodal sarcasm detection is pronounced. Aiming to bridge the cross-modal semantic gap, semantic enhancement is performed by using a multiple contrastive learning strategy. Experiments were conducted on a benchmark dataset. Our model outperforms the latest baseline by 1.87% in terms of the F1-score and 1% in terms of accuracy.
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title_short	A Semantic Enhancement Framework for Multimodal Sarcasm Detection
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