@inproceedings{agrawal-etal-2025-dense,
title = "Dense Retrieval with Quantity Comparison Intent",
author = "Agrawal, Prayas and
M, Nandeesh Kumar K and
Chelliah, Muthusamy and
Kumar, Surender and
Chakrabarti, Soumen",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1220/",
doi = "10.18653/v1/2025.findings-acl.1220",
pages = "23825--23839",
ISBN = "979-8-89176-256-5",
abstract = "Pre-trained language models (PLMs) fragment numerals and units that express quantities in arbitrary ways, depending on their subword vocabulary. Consequently, they are unable to contextualize the fragment embeddings well enough to be proficient with dense retrieval in domains like e-commerce and finance. Arithmetic inequality constraints ({``}laptop under 2 lb'') offer additional challenges. In response, we propose DeepQuant, a dense retrieval system built around a dense multi-vector index, but carefully engineered to elicit and exploit quantities and associated comparison intents. A novel component of our relevance score compares two quantities with compatible units, conditioned on a proposed comparison operator. The uncertain extractions of numerals, units and comparators are marginalized in a suitable manner. On two public and one proprietary e-commerce benchmark, DeepQuant is both faster and more accurate than popular PLMs. It also beats several competitive sparse and dense retrieval systems that do not take special cognizance of quantities."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="agrawal-etal-2025-dense">
<titleInfo>
<title>Dense Retrieval with Quantity Comparison Intent</title>
</titleInfo>
<name type="personal">
<namePart type="given">Prayas</namePart>
<namePart type="family">Agrawal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nandeesh</namePart>
<namePart type="given">Kumar</namePart>
<namePart type="given">K</namePart>
<namePart type="family">M</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muthusamy</namePart>
<namePart type="family">Chelliah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Surender</namePart>
<namePart type="family">Kumar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Soumen</namePart>
<namePart type="family">Chakrabarti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Nabende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Taher</namePart>
<namePart type="family">Pilehvar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-256-5</identifier>
</relatedItem>
<abstract>Pre-trained language models (PLMs) fragment numerals and units that express quantities in arbitrary ways, depending on their subword vocabulary. Consequently, they are unable to contextualize the fragment embeddings well enough to be proficient with dense retrieval in domains like e-commerce and finance. Arithmetic inequality constraints (“laptop under 2 lb”) offer additional challenges. In response, we propose DeepQuant, a dense retrieval system built around a dense multi-vector index, but carefully engineered to elicit and exploit quantities and associated comparison intents. A novel component of our relevance score compares two quantities with compatible units, conditioned on a proposed comparison operator. The uncertain extractions of numerals, units and comparators are marginalized in a suitable manner. On two public and one proprietary e-commerce benchmark, DeepQuant is both faster and more accurate than popular PLMs. It also beats several competitive sparse and dense retrieval systems that do not take special cognizance of quantities.</abstract>
<identifier type="citekey">agrawal-etal-2025-dense</identifier>
<identifier type="doi">10.18653/v1/2025.findings-acl.1220</identifier>
<location>
<url>https://aclanthology.org/2025.findings-acl.1220/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>23825</start>
<end>23839</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Dense Retrieval with Quantity Comparison Intent
%A Agrawal, Prayas
%A M, Nandeesh Kumar K.
%A Chelliah, Muthusamy
%A Kumar, Surender
%A Chakrabarti, Soumen
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F agrawal-etal-2025-dense
%X Pre-trained language models (PLMs) fragment numerals and units that express quantities in arbitrary ways, depending on their subword vocabulary. Consequently, they are unable to contextualize the fragment embeddings well enough to be proficient with dense retrieval in domains like e-commerce and finance. Arithmetic inequality constraints (“laptop under 2 lb”) offer additional challenges. In response, we propose DeepQuant, a dense retrieval system built around a dense multi-vector index, but carefully engineered to elicit and exploit quantities and associated comparison intents. A novel component of our relevance score compares two quantities with compatible units, conditioned on a proposed comparison operator. The uncertain extractions of numerals, units and comparators are marginalized in a suitable manner. On two public and one proprietary e-commerce benchmark, DeepQuant is both faster and more accurate than popular PLMs. It also beats several competitive sparse and dense retrieval systems that do not take special cognizance of quantities.
%R 10.18653/v1/2025.findings-acl.1220
%U https://aclanthology.org/2025.findings-acl.1220/
%U https://doi.org/10.18653/v1/2025.findings-acl.1220
%P 23825-23839
Markdown (Informal)
[Dense Retrieval with Quantity Comparison Intent](https://aclanthology.org/2025.findings-acl.1220/) (Agrawal et al., Findings 2025)
ACL
- Prayas Agrawal, Nandeesh Kumar K M, Muthusamy Chelliah, Surender Kumar, and Soumen Chakrabarti. 2025. Dense Retrieval with Quantity Comparison Intent. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23825–23839, Vienna, Austria. Association for Computational Linguistics.