<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="das-etal-2022-adbert">
    <titleInfo>
        <title>AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Debarati</namePart>
        <namePart type="family">Das</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Roopana</namePart>
        <namePart type="family">Chenchu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maral</namePart>
        <namePart type="family">Abdollahi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jisu</namePart>
        <namePart type="family">Huh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jaideep</namePart>
        <namePart type="family">Srivastava</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technical challenges. Specifically, given a televised event and related tweets about this event, we need methods to effectively align these tweets and the corresponding event. In this paper, we consider the specific ecosystem of the Superbowl 2020 and map viewer tweets to advertisements they are referring to. Our proposed model, AdBERT, is an effective few-shot learning framework that is able to handle the technical challenges of establishing ad-relatedness, class imbalance as well as the scarcity of labeled data. As part of this study, we have curated and developed two datasets that can prove to be useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements. Explaining connections to SentenceBERT, we describe the advantages of AdBERT that allow us to make the most out of a challenging and interesting dataset which we will open-source along with the models developed in this paper.</abstract>
    <identifier type="citekey">das-etal-2022-adbert</identifier>
    <location>
        <url>https://aclanthology.org/2022.wnut-1.19</url>
    </location>
    <part>
        <date>2022-10</date>
        <extent unit="page">
            <start>171</start>
            <end>179</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements
%A Das, Debarati
%A Chenchu, Roopana
%A Abdollahi, Maral
%A Huh, Jisu
%A Srivastava, Jaideep
%S Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F das-etal-2022-adbert
%X The tremendous increase in social media usage for sharing Television (TV) experiences has provided a unique opportunity in the Public Health and Marketing sectors to understand viewer engagement and attitudes through viewer-generated content on social media. However, this opportunity also comes with associated technical challenges. Specifically, given a televised event and related tweets about this event, we need methods to effectively align these tweets and the corresponding event. In this paper, we consider the specific ecosystem of the Superbowl 2020 and map viewer tweets to advertisements they are referring to. Our proposed model, AdBERT, is an effective few-shot learning framework that is able to handle the technical challenges of establishing ad-relatedness, class imbalance as well as the scarcity of labeled data. As part of this study, we have curated and developed two datasets that can prove to be useful for Social TV research: 1) dataset of ad-related tweets and 2) dataset of ad descriptions of Superbowl advertisements. Explaining connections to SentenceBERT, we describe the advantages of AdBERT that allow us to make the most out of a challenging and interesting dataset which we will open-source along with the models developed in this paper.
%U https://aclanthology.org/2022.wnut-1.19
%P 171-179

Markdown (Informal)

[AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements](https://aclanthology.org/2022.wnut-1.19) (Das et al., WNUT 2022)

• AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements (Das et al., WNUT 2022)

ACL

• Debarati Das, Roopana Chenchu, Maral Abdollahi, Jisu Huh, and Jaideep Srivastava. 2022. AdBERT: An Effective Few Shot Learning Framework for Aligning Tweets to Superbowl Advertisements. In Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022), pages 171–179, Gyeongju, Republic of Korea. Association for Computational Linguistics.