@inproceedings{xiaoming-etal-2023-sentbench,
title = "{S}ent{B}ench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction",
author = "Xiaoming, Liu and
Hongyu, Lin and
Xianpei, Han and
Le, Sun",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.69",
pages = "813--823",
abstract = "{``}Self-supervised learning has been widely used to learn effective sentence representations. Previ-ous evaluation of sentence representations mainly focuses on the limited combination of tasks andparadigms while failing to evaluate their effectiveness in a wider range of application scenarios. Such divergences prevent us from understanding the limitations of current sentence representa-tions, as well as the connections between learning approaches and downstream applications. Inthis paper, we propose SentBench, a new comprehensive benchmark to evaluate sentence repre-sentations. SentBench covers 12 kinds of tasks and evaluates sentence representations with threetypes of different downstream application paradigms. Based on SentBench, we re-evaluate sev-eral frequently used self-supervised sentence representation learning approaches. Experimentsshow that SentBench can effectively evaluate sentence representations from multiple perspec-tives, and the performance on SentBench leads to some novel findings which enlighten futureresearches.{''}",
language = "English",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xiaoming-etal-2023-sentbench">
<titleInfo>
<title>SentBench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Liu</namePart>
<namePart type="family">Xiaoming</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lin</namePart>
<namePart type="family">Hongyu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Han</namePart>
<namePart type="family">Xianpei</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sun</namePart>
<namePart type="family">Le</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-08</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<language>
<languageTerm type="text">English</languageTerm>
<languageTerm type="code" authority="iso639-2b">eng</languageTerm>
</language>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maosong</namePart>
<namePart type="family">Sun</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bing</namePart>
<namePart type="family">Qin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xipeng</namePart>
<namePart type="family">Qiu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jing</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xianpei</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Chinese Information Processing Society of China</publisher>
<place>
<placeTerm type="text">Harbin, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>“Self-supervised learning has been widely used to learn effective sentence representations. Previ-ous evaluation of sentence representations mainly focuses on the limited combination of tasks andparadigms while failing to evaluate their effectiveness in a wider range of application scenarios. Such divergences prevent us from understanding the limitations of current sentence representa-tions, as well as the connections between learning approaches and downstream applications. Inthis paper, we propose SentBench, a new comprehensive benchmark to evaluate sentence repre-sentations. SentBench covers 12 kinds of tasks and evaluates sentence representations with threetypes of different downstream application paradigms. Based on SentBench, we re-evaluate sev-eral frequently used self-supervised sentence representation learning approaches. Experimentsshow that SentBench can effectively evaluate sentence representations from multiple perspec-tives, and the performance on SentBench leads to some novel findings which enlighten futureresearches.”</abstract>
<identifier type="citekey">xiaoming-etal-2023-sentbench</identifier>
<location>
<url>https://aclanthology.org/2023.ccl-1.69</url>
</location>
<part>
<date>2023-08</date>
<extent unit="page">
<start>813</start>
<end>823</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T SentBench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction
%A Xiaoming, Liu
%A Hongyu, Lin
%A Xianpei, Han
%A Le, Sun
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F xiaoming-etal-2023-sentbench
%X “Self-supervised learning has been widely used to learn effective sentence representations. Previ-ous evaluation of sentence representations mainly focuses on the limited combination of tasks andparadigms while failing to evaluate their effectiveness in a wider range of application scenarios. Such divergences prevent us from understanding the limitations of current sentence representa-tions, as well as the connections between learning approaches and downstream applications. Inthis paper, we propose SentBench, a new comprehensive benchmark to evaluate sentence repre-sentations. SentBench covers 12 kinds of tasks and evaluates sentence representations with threetypes of different downstream application paradigms. Based on SentBench, we re-evaluate sev-eral frequently used self-supervised sentence representation learning approaches. Experimentsshow that SentBench can effectively evaluate sentence representations from multiple perspec-tives, and the performance on SentBench leads to some novel findings which enlighten futureresearches.”
%U https://aclanthology.org/2023.ccl-1.69
%P 813-823
Markdown (Informal)
[SentBench: Comprehensive Evaluation of Self-Supervised Sentence Representation with Benchmark Construction](https://aclanthology.org/2023.ccl-1.69) (Xiaoming et al., CCL 2023)
ACL