<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mantri-etal-2026-hierarchical">
    <titleInfo>
        <title>Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yoages</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Mantri</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yagneswaran</namePart>
        <namePart type="family">Sriraja</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">John</namePart>
        <namePart type="family">Hartley</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Greig</namePart>
        <namePart type="given">A</namePart>
        <namePart type="family">Cowan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Raad</namePart>
        <namePart type="family">Khraishi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Abhinav</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yunyao</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Georg</namePart>
            <namePart type="family">Rehm</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mei</namePart>
            <namePart type="family">Tu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-394-4</identifier>
    </relatedItem>
    <abstract>Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.</abstract>
    <identifier type="citekey">mantri-etal-2026-hierarchical</identifier>
    <location>
        <url>https://aclanthology.org/2026.acl-industry.108/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>1555</start>
            <end>1566</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction
%A Mantri, Yoages Kumar
%A Sriraja, Yagneswaran
%A Hartley, John
%A Cowan, Greig A.
%A Khraishi, Raad
%A Gupta, Abhinav
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F mantri-etal-2026-hierarchical
%X Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.
%U https://aclanthology.org/2026.acl-industry.108/
%P 1555-1566

Markdown (Informal)

[Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction](https://aclanthology.org/2026.acl-industry.108/) (Mantri et al., ACL 2026)

• Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction (Mantri et al., ACL 2026)

ACL

• Yoages Kumar Mantri, Yagneswaran Sriraja, John Hartley, Greig A Cowan, Raad Khraishi, and Abhinav Gupta. 2026. Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1555–1566, San Diego, California, USA. Association for Computational Linguistics.