<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jo-2023-self-supervised">
    <titleInfo>
        <title>Self-supervised Post-processing Method to Enrich Pretrained Word Vectors</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hwiyeol</namePart>
        <namePart type="family">Jo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Retrofitting techniques, which inject external resources into word representations, have compensated for the weakness of distributed representations in semantic and relational knowledge between words. However, the previous methods require additional external resources and strongly depend on the lexicon. To address the issues, we propose a simple extension of extrofitting, self-supervised extrofitting: extrofitting by its own word vector distribution. Our methods improve the vanilla embeddings on all of word similarity tasks without any external resources. Moreover, the method is also effective in various languages, which implies that our method will be useful in lexicon-scarce languages. As downstream tasks, we show its benefits in dialogue state tracking and text classification tasks, reporting better and generalized results compared to other word vector specialization methods.</abstract>
    <identifier type="citekey">jo-2023-self-supervised</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.54</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.54</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>747</start>
            <end>757</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Self-supervised Post-processing Method to Enrich Pretrained Word Vectors
%A Jo, Hwiyeol
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jo-2023-self-supervised
%X Retrofitting techniques, which inject external resources into word representations, have compensated for the weakness of distributed representations in semantic and relational knowledge between words. However, the previous methods require additional external resources and strongly depend on the lexicon. To address the issues, we propose a simple extension of extrofitting, self-supervised extrofitting: extrofitting by its own word vector distribution. Our methods improve the vanilla embeddings on all of word similarity tasks without any external resources. Moreover, the method is also effective in various languages, which implies that our method will be useful in lexicon-scarce languages. As downstream tasks, we show its benefits in dialogue state tracking and text classification tasks, reporting better and generalized results compared to other word vector specialization methods.
%R 10.18653/v1/2023.findings-emnlp.54
%U https://aclanthology.org/2023.findings-emnlp.54
%U https://doi.org/10.18653/v1/2023.findings-emnlp.54
%P 747-757

Markdown (Informal)

[Self-supervised Post-processing Method to Enrich Pretrained Word Vectors](https://aclanthology.org/2023.findings-emnlp.54) (Jo, Findings 2023)

• Self-supervised Post-processing Method to Enrich Pretrained Word Vectors (Jo, Findings 2023)

ACL

• Hwiyeol Jo. 2023. Self-supervised Post-processing Method to Enrich Pretrained Word Vectors. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 747–757, Singapore. Association for Computational Linguistics.