@inproceedings{cavalin-pinhanez-2024-theoretical-empirical,
title = "Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios",
author = "Cavalin, Paulo and
Pinhanez, Claudio Santos",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1391",
pages = "16000--16013",
abstract = "This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work has shown that there can be significant improvements in OOS detection when the intent classes are represented as dense-vectors based on domain-specific knowledge, we argue in this paper that such gains are more likely due to advantages of the much richer topologies that can be created with dense vectors compared to the equidistant class representation assumed by one-hot encodings. We start by demonstrating how dense-vector encodings are able to create OOS spaces with much richer topologies. Then, we show empirically, using four standard intent classification datasets, that knowledge-free, randomly generated dense-vector encodings of intent classes can yield over 20{\%} gains over one-hot encodings, producing better systems for open-world classification tasks, mostly from improvements in OOS detection.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cavalin-pinhanez-2024-theoretical-empirical">
<titleInfo>
<title>Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios</title>
</titleInfo>
<name type="personal">
<namePart type="given">Paulo</namePart>
<namePart type="family">Cavalin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claudio</namePart>
<namePart type="given">Santos</namePart>
<namePart type="family">Pinhanez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work has shown that there can be significant improvements in OOS detection when the intent classes are represented as dense-vectors based on domain-specific knowledge, we argue in this paper that such gains are more likely due to advantages of the much richer topologies that can be created with dense vectors compared to the equidistant class representation assumed by one-hot encodings. We start by demonstrating how dense-vector encodings are able to create OOS spaces with much richer topologies. Then, we show empirically, using four standard intent classification datasets, that knowledge-free, randomly generated dense-vector encodings of intent classes can yield over 20% gains over one-hot encodings, producing better systems for open-world classification tasks, mostly from improvements in OOS detection.</abstract>
<identifier type="citekey">cavalin-pinhanez-2024-theoretical-empirical</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.1391</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>16000</start>
<end>16013</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios
%A Cavalin, Paulo
%A Pinhanez, Claudio Santos
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cavalin-pinhanez-2024-theoretical-empirical
%X This work explores the intrinsic limitations of the popular one-hot encoding method in classification of intents when detection of out-of-scope (OOS) inputs is required. Although recent work has shown that there can be significant improvements in OOS detection when the intent classes are represented as dense-vectors based on domain-specific knowledge, we argue in this paper that such gains are more likely due to advantages of the much richer topologies that can be created with dense vectors compared to the equidistant class representation assumed by one-hot encodings. We start by demonstrating how dense-vector encodings are able to create OOS spaces with much richer topologies. Then, we show empirically, using four standard intent classification datasets, that knowledge-free, randomly generated dense-vector encodings of intent classes can yield over 20% gains over one-hot encodings, producing better systems for open-world classification tasks, mostly from improvements in OOS detection.
%U https://aclanthology.org/2024.lrec-main.1391
%P 16000-16013
Markdown (Informal)
[Theoretical and Empirical Advantages of Dense-Vector to One-Hot Encoding of Intent Classes in Open-World Scenarios](https://aclanthology.org/2024.lrec-main.1391) (Cavalin & Pinhanez, LREC-COLING 2024)
ACL