<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cabello-akujuobi-2024-simple">
    <titleInfo>
        <title>It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Laura</namePart>
        <namePart type="family">Cabello</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Uchenna</namePart>
        <namePart type="family">Akujuobi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we build upon the instruction tuned model proposed by Scaria et al. (2023), who present an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. We propose PFInstruct, an extension to this instruction learning paradigm by appending an NLP-related task prefix to the task description. This simple approach leads to improved performance across all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average of +5.43 F1-score across SemEval datasets. Furthermore, we explore the impact of the prefix-enhanced prompt quality on the ABSA subtasks and find that even a noisy prefix enhances model performance compared to the baseline. Our method also achieves competitive results on a biomedical domain dataset (ERSA).</abstract>
    <identifier type="citekey">cabello-akujuobi-2024-simple</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.394</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>6597</start>
            <end>6610</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance
%A Cabello, Laura
%A Akujuobi, Uchenna
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F cabello-akujuobi-2024-simple
%X Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for these subtasks. In this paper, we build upon the instruction tuned model proposed by Scaria et al. (2023), who present an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. We propose PFInstruct, an extension to this instruction learning paradigm by appending an NLP-related task prefix to the task description. This simple approach leads to improved performance across all tested SemEval subtasks, surpassing previous state-of-the-art (SOTA) on the ATE subtask (Rest14) by +3.28 F1-score, and on the AOOE subtask by an average of +5.43 F1-score across SemEval datasets. Furthermore, we explore the impact of the prefix-enhanced prompt quality on the ABSA subtasks and find that even a noisy prefix enhances model performance compared to the baseline. Our method also achieves competitive results on a biomedical domain dataset (ERSA).
%U https://aclanthology.org/2024.findings-acl.394
%P 6597-6610

Markdown (Informal)

[It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance](https://aclanthology.org/2024.findings-acl.394) (Cabello & Akujuobi, Findings 2024)

• It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance (Cabello & Akujuobi, Findings 2024)

ACL

• Laura Cabello and Uchenna Akujuobi. 2024. It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance. In Findings of the Association for Computational Linguistics ACL 2024, pages 6597–6610, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.