<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ranjan-etal-2022-progressive">
    <titleInfo>
        <title>Progressive Sentiment Analysis for Code-Switched Text Data</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sudhanshu</namePart>
        <namePart type="family">Ranjan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dheeraj</namePart>
        <namePart type="family">Mekala</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jingbo</namePart>
        <namePart type="family">Shang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2022</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition.However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges:(1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can’t be applied efficiently,(2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account.Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples.</abstract>
    <identifier type="citekey">ranjan-etal-2022-progressive</identifier>
    <identifier type="doi">10.18653/v1/2022.findings-emnlp.82</identifier>
    <location>
        <url>https://aclanthology.org/2022.findings-emnlp.82</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>1155</start>
            <end>1167</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Progressive Sentiment Analysis for Code-Switched Text Data
%A Ranjan, Sudhanshu
%A Mekala, Dheeraj
%A Shang, Jingbo
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ranjan-etal-2022-progressive
%X Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition.However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges:(1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can’t be applied efficiently,(2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account.Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples.
%R 10.18653/v1/2022.findings-emnlp.82
%U https://aclanthology.org/2022.findings-emnlp.82
%U https://doi.org/10.18653/v1/2022.findings-emnlp.82
%P 1155-1167

Markdown (Informal)

[Progressive Sentiment Analysis for Code-Switched Text Data](https://aclanthology.org/2022.findings-emnlp.82) (Ranjan et al., Findings 2022)

• Progressive Sentiment Analysis for Code-Switched Text Data (Ranjan et al., Findings 2022)

ACL

• Sudhanshu Ranjan, Dheeraj Mekala, and Jingbo Shang. 2022. Progressive Sentiment Analysis for Code-Switched Text Data. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1155–1167, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.