@inproceedings{sarkar-etal-2022-exploring,
title = "{E}xploring Universal Sentence Encoders for Zero-shot Text Classification",
author = "Sarkar, Souvika and
Feng, Dongji and
Karmaker Santu, Shubhra Kanti",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-short.18",
pages = "135--147",
abstract = "Universal Sentence Encoder (USE) has gained much popularity recently as a general-purpose sentence encoding technique. As the name suggests, USE is designed to be fairly general and has indeed been shown to achieve superior performances for many downstream NLP tasks. In this paper, we present an interesting {``}negative{''} result on USE in the context of zero-shot text classification, a challenging task, which has recently gained much attraction. More specifically, we found some interesting cases of zero-shot text classification, where topic based inference outperformed USE-based inference in terms of F1 score. Further investigation revealed that USE struggles to perform well on data-sets with a large number of labels with high semantic overlaps, while topic-based classification works well for the same.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sarkar-etal-2022-exploring">
<titleInfo>
<title>Exploring Universal Sentence Encoders for Zero-shot Text Classification</title>
</titleInfo>
<name type="personal">
<namePart type="given">Souvika</namePart>
<namePart type="family">Sarkar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dongji</namePart>
<namePart type="family">Feng</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shubhra</namePart>
<namePart type="given">Kanti</namePart>
<namePart type="family">Karmaker Santu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yulan</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Heng</namePart>
<namePart type="family">Ji</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sujian</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chua-Hui</namePart>
<namePart type="family">Chang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online only</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Universal Sentence Encoder (USE) has gained much popularity recently as a general-purpose sentence encoding technique. As the name suggests, USE is designed to be fairly general and has indeed been shown to achieve superior performances for many downstream NLP tasks. In this paper, we present an interesting “negative” result on USE in the context of zero-shot text classification, a challenging task, which has recently gained much attraction. More specifically, we found some interesting cases of zero-shot text classification, where topic based inference outperformed USE-based inference in terms of F1 score. Further investigation revealed that USE struggles to perform well on data-sets with a large number of labels with high semantic overlaps, while topic-based classification works well for the same.</abstract>
<identifier type="citekey">sarkar-etal-2022-exploring</identifier>
<location>
<url>https://aclanthology.org/2022.aacl-short.18</url>
</location>
<part>
<date>2022-11</date>
<extent unit="page">
<start>135</start>
<end>147</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Exploring Universal Sentence Encoders for Zero-shot Text Classification
%A Sarkar, Souvika
%A Feng, Dongji
%A Karmaker Santu, Shubhra Kanti
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F sarkar-etal-2022-exploring
%X Universal Sentence Encoder (USE) has gained much popularity recently as a general-purpose sentence encoding technique. As the name suggests, USE is designed to be fairly general and has indeed been shown to achieve superior performances for many downstream NLP tasks. In this paper, we present an interesting “negative” result on USE in the context of zero-shot text classification, a challenging task, which has recently gained much attraction. More specifically, we found some interesting cases of zero-shot text classification, where topic based inference outperformed USE-based inference in terms of F1 score. Further investigation revealed that USE struggles to perform well on data-sets with a large number of labels with high semantic overlaps, while topic-based classification works well for the same.
%U https://aclanthology.org/2022.aacl-short.18
%P 135-147
Markdown (Informal)
[Exploring Universal Sentence Encoders for Zero-shot Text Classification](https://aclanthology.org/2022.aacl-short.18) (Sarkar et al., AACL-IJCNLP 2022)
ACL
- Souvika Sarkar, Dongji Feng, and Shubhra Kanti Karmaker Santu. 2022. Exploring Universal Sentence Encoders for Zero-shot Text Classification. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 135–147, Online only. Association for Computational Linguistics.