<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kirti-etal-2026-vrittanta">
    <titleInfo>
        <title>Vrittanta-AS: Dataset Development and Benchmarking for Event Trigger Detection and Classification in Assamese</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Chaitanya</namePart>
        <namePart type="family">Kirti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dhrubajyoti</namePart>
        <namePart type="family">Pathak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ashish</namePart>
        <namePart type="family">Anand</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Prithwijit</namePart>
        <namePart type="family">Guha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Event trigger detection and classification aim to identify and categorize events within unstructured text. While prior research has primarily focused on news or biomedical corpora, the literary domain, especially short stories, remains largely underexplored. This gap is particularly pronounced for low-resource languages such as Assamese, where limited annotated data and complex narrative structures hinder progress. To address this challenge, we introduce Vrittanta-AS, a manually curated Assamese event trigger detection and classification dataset comprising 13,171 annotated events extracted from short stories. The dataset is designed to advance research in information extraction and narrative understanding for low-resource Indian languages. We conduct a comprehensive evaluation using classical machine learning methods, neural sequential architectures, pre-trained transformer models, and large language models (LLMs) on the proposed dataset. Experimental results demonstrate that IndicBERT v2 achieves the highest performance for both event trigger detection (85.86% micro-F1) and classification (65.21% macro-F1). Vrittanta-AS serves as an important step toward developing benchmark resources for event trigger detection and classification in Assamese literary text.</abstract>
    <identifier type="citekey">kirti-etal-2026-vrittanta</identifier>
    <identifier type="doi">10.63317/5ieyiqjkvgxt</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.601/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>7581</start>
            <end>7591</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Vrittanta-AS: Dataset Development and Benchmarking for Event Trigger Detection and Classification in Assamese
%A Kirti, Chaitanya
%A Pathak, Dhrubajyoti
%A Anand, Ashish
%A Guha, Prithwijit
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F kirti-etal-2026-vrittanta
%X Event trigger detection and classification aim to identify and categorize events within unstructured text. While prior research has primarily focused on news or biomedical corpora, the literary domain, especially short stories, remains largely underexplored. This gap is particularly pronounced for low-resource languages such as Assamese, where limited annotated data and complex narrative structures hinder progress. To address this challenge, we introduce Vrittanta-AS, a manually curated Assamese event trigger detection and classification dataset comprising 13,171 annotated events extracted from short stories. The dataset is designed to advance research in information extraction and narrative understanding for low-resource Indian languages. We conduct a comprehensive evaluation using classical machine learning methods, neural sequential architectures, pre-trained transformer models, and large language models (LLMs) on the proposed dataset. Experimental results demonstrate that IndicBERT v2 achieves the highest performance for both event trigger detection (85.86% micro-F1) and classification (65.21% macro-F1). Vrittanta-AS serves as an important step toward developing benchmark resources for event trigger detection and classification in Assamese literary text.
%R 10.63317/5ieyiqjkvgxt
%U https://aclanthology.org/2026.lrec-1.601/
%U https://doi.org/10.63317/5ieyiqjkvgxt
%P 7581-7591

Markdown (Informal)

[Vrittanta-AS: Dataset Development and Benchmarking for Event Trigger Detection and Classification in Assamese](https://aclanthology.org/2026.lrec-1.601/) (Kirti et al., LREC 2026)

• Vrittanta-AS: Dataset Development and Benchmarking for Event Trigger Detection and Classification in Assamese (Kirti et al., LREC 2026)

ACL

• Chaitanya Kirti, Dhrubajyoti Pathak, Ashish Anand, and Prithwijit Guha. 2026. Vrittanta-AS: Dataset Development and Benchmarking for Event Trigger Detection and Classification in Assamese. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 7581–7591, Palma de Mallorca, Spain. ELRA Language Resource Association.