<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gorodissky-etal-2025-cross">
    <titleInfo>
        <title>Cross-Lingual Extractive Question Answering with Unanswerable Questions</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuval</namePart>
        <namePart type="family">Gorodissky</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Elior</namePart>
        <namePart type="family">Sulem</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Roth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lea</namePart>
            <namePart type="family">Frermann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mark</namePart>
            <namePart type="family">Stevenson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Suzhou, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-340-1</identifier>
    </relatedItem>
    <abstract>Cross-lingual Extractive Question Answering (EQA) extends standard EQA by requiring models to find answers in passages written in languages different from the questions. The Generalized Cross-Lingual Transfer (G-XLT) task evaluates models’ zero-shot ability to transfer question answering capabilities across languages using only English training data. While previous research has primarily focused on scenarios where answers are always present, real-world applications often encounter situations where no answer exists within the given context. This paper introduces an enhanced G-XLT task definition that explicitly handles unanswerable questions, bridging a critical gap in current research. To address this challenge, we present two new datasets: miXQuAD and MLQA-IDK, which address both answerable and unanswerable questions and respectively cover 12 and 7 language pairs. Our study evaluates state-of-the-art large language models using fine-tuning, parameter-efficient techniques, and in-context learning approaches, revealing interesting trade-offs between a smaller fine-tuned model’s performance on answerable questions versus a larger in-context learning model’s capability on unanswerable questions. We also examine language similarity patterns based on model performance, finding alignments with known language families.</abstract>
    <identifier type="citekey">gorodissky-etal-2025-cross</identifier>
    <location>
        <url>https://aclanthology.org/2025.starsem-1.8/</url>
    </location>
    <part>
        <date>2025-11</date>
        <extent unit="page">
            <start>100</start>
            <end>121</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Cross-Lingual Extractive Question Answering with Unanswerable Questions
%A Gorodissky, Yuval
%A Sulem, Elior
%A Roth, Dan
%Y Frermann, Lea
%Y Stevenson, Mark
%S Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-340-1
%F gorodissky-etal-2025-cross
%X Cross-lingual Extractive Question Answering (EQA) extends standard EQA by requiring models to find answers in passages written in languages different from the questions. The Generalized Cross-Lingual Transfer (G-XLT) task evaluates models’ zero-shot ability to transfer question answering capabilities across languages using only English training data. While previous research has primarily focused on scenarios where answers are always present, real-world applications often encounter situations where no answer exists within the given context. This paper introduces an enhanced G-XLT task definition that explicitly handles unanswerable questions, bridging a critical gap in current research. To address this challenge, we present two new datasets: miXQuAD and MLQA-IDK, which address both answerable and unanswerable questions and respectively cover 12 and 7 language pairs. Our study evaluates state-of-the-art large language models using fine-tuning, parameter-efficient techniques, and in-context learning approaches, revealing interesting trade-offs between a smaller fine-tuned model’s performance on answerable questions versus a larger in-context learning model’s capability on unanswerable questions. We also examine language similarity patterns based on model performance, finding alignments with known language families.
%U https://aclanthology.org/2025.starsem-1.8/
%P 100-121

Markdown (Informal)

[Cross-Lingual Extractive Question Answering with Unanswerable Questions](https://aclanthology.org/2025.starsem-1.8/) (Gorodissky et al., *SEM 2025)

• Cross-Lingual Extractive Question Answering with Unanswerable Questions (Gorodissky et al., *SEM 2025)

ACL

• Yuval Gorodissky, Elior Sulem, and Dan Roth. 2025. Cross-Lingual Extractive Question Answering with Unanswerable Questions. In Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025), pages 100–121, Suzhou, China. Association for Computational Linguistics.