@inproceedings{venkatesh-raman-2024-bits,
title = "{BITS} Pilani at {S}em{E}val-2024 Task 1: Using text-embedding-3-large and {L}a{BSE} embeddings for Semantic Textual Relatedness",
author = "Venkatesh, Dilip and
Raman, Sundaresan",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.124",
doi = "10.18653/v1/2024.semeval-1.124",
pages = "865--868",
abstract = "Semantic Relatedness of a pair of text (sentences or words) is the degree to which theirmeanings are close. The Track A of the Semantic Textual Relatedness shared task aimsto find the semantic relatedness for the English language along with multiple other lowresource languages with the use of pretrainedlanguage models. We proposes a system tofind the Spearman coefficient of a textual pairusing pretrained embedding models like textembedding-3-large and LaBSE.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="venkatesh-raman-2024-bits">
<titleInfo>
<title>BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dilip</namePart>
<namePart type="family">Venkatesh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sundaresan</namePart>
<namePart type="family">Raman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Atul</namePart>
<namePart type="given">Kr.</namePart>
<namePart type="family">Ojha</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">A</namePart>
<namePart type="given">Seza</namePart>
<namePart type="family">Doğruöz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Harish</namePart>
<namePart type="family">Tayyar Madabushi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Da San Martino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sara</namePart>
<namePart type="family">Rosenthal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Aiala</namePart>
<namePart type="family">Rosá</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Mexico City, Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Semantic Relatedness of a pair of text (sentences or words) is the degree to which theirmeanings are close. The Track A of the Semantic Textual Relatedness shared task aimsto find the semantic relatedness for the English language along with multiple other lowresource languages with the use of pretrainedlanguage models. We proposes a system tofind the Spearman coefficient of a textual pairusing pretrained embedding models like textembedding-3-large and LaBSE.</abstract>
<identifier type="citekey">venkatesh-raman-2024-bits</identifier>
<identifier type="doi">10.18653/v1/2024.semeval-1.124</identifier>
<location>
<url>https://aclanthology.org/2024.semeval-1.124</url>
</location>
<part>
<date>2024-06</date>
<extent unit="page">
<start>865</start>
<end>868</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness
%A Venkatesh, Dilip
%A Raman, Sundaresan
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F venkatesh-raman-2024-bits
%X Semantic Relatedness of a pair of text (sentences or words) is the degree to which theirmeanings are close. The Track A of the Semantic Textual Relatedness shared task aimsto find the semantic relatedness for the English language along with multiple other lowresource languages with the use of pretrainedlanguage models. We proposes a system tofind the Spearman coefficient of a textual pairusing pretrained embedding models like textembedding-3-large and LaBSE.
%R 10.18653/v1/2024.semeval-1.124
%U https://aclanthology.org/2024.semeval-1.124
%U https://doi.org/10.18653/v1/2024.semeval-1.124
%P 865-868
Markdown (Informal)
[BITS Pilani at SemEval-2024 Task 1: Using text-embedding-3-large and LaBSE embeddings for Semantic Textual Relatedness](https://aclanthology.org/2024.semeval-1.124) (Venkatesh & Raman, SemEval 2024)
ACL