<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jana-etal-2024-continuous">
    <titleInfo>
        <title>Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Soumyadeep</namePart>
        <namePart type="family">Jana</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Animesh</namePart>
        <namePart type="family">Dey</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ranbir</namePart>
        <namePart type="given">Singh</namePart>
        <namePart type="family">Sanasam</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 28th Conference on Computational Natural Language Learning</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Libby</namePart>
            <namePart type="family">Barak</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Malihe</namePart>
            <namePart type="family">Alikhani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, FL, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>With the steep rise in multimodal content on social media, multimodal sarcasm detection has gained widespread attention from research communities. Existing studies depend on large-scale data, which is challenging to obtain and expensive to annotate. Thus, investigating this problem in a few-shot scenario is required. Overtly complex multimodal models are prone to overfitting on in-domain data, which hampers their performance on out-of-distribution (OOD) data. To address these issues, we propose Continuous Attentive Multimodal Prompt Tuning model (CAMP), that leverages the prompt tuning paradigm to handle few-shot multimodal sarcasm detection. To overcome the siloed learning process of continuous prompt tokens, we design a novel, continuous multimodal attentive prompt where the continuous tokens intricately engage with both image and text tokens, enabling the assimilation of knowledge from different input modalities. Experimental results indicate that our method outperforms other multimodal baseline methods in the few-shot setting and OOD scenarios.</abstract>
    <identifier type="citekey">jana-etal-2024-continuous</identifier>
    <location>
        <url>https://aclanthology.org/2024.conll-1.25</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>314</start>
            <end>326</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection
%A Jana, Soumyadeep
%A Dey, Animesh
%A Sanasam, Ranbir Singh
%Y Barak, Libby
%Y Alikhani, Malihe
%S Proceedings of the 28th Conference on Computational Natural Language Learning
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F jana-etal-2024-continuous
%X With the steep rise in multimodal content on social media, multimodal sarcasm detection has gained widespread attention from research communities. Existing studies depend on large-scale data, which is challenging to obtain and expensive to annotate. Thus, investigating this problem in a few-shot scenario is required. Overtly complex multimodal models are prone to overfitting on in-domain data, which hampers their performance on out-of-distribution (OOD) data. To address these issues, we propose Continuous Attentive Multimodal Prompt Tuning model (CAMP), that leverages the prompt tuning paradigm to handle few-shot multimodal sarcasm detection. To overcome the siloed learning process of continuous prompt tokens, we design a novel, continuous multimodal attentive prompt where the continuous tokens intricately engage with both image and text tokens, enabling the assimilation of knowledge from different input modalities. Experimental results indicate that our method outperforms other multimodal baseline methods in the few-shot setting and OOD scenarios.
%U https://aclanthology.org/2024.conll-1.25
%P 314-326

Markdown (Informal)

[Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection](https://aclanthology.org/2024.conll-1.25) (Jana et al., CoNLL 2024)

• Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection (Jana et al., CoNLL 2024)

ACL

• Soumyadeep Jana, Animesh Dey, and Ranbir Singh Sanasam. 2024. Continuous Attentive Multimodal Prompt Tuning for Few-Shot Multimodal Sarcasm Detection. In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 314–326, Miami, FL, USA. Association for Computational Linguistics.