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        <title>Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation</title>
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        <namePart type="given">Murhaf</namePart>
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    <name type="personal">
        <namePart type="given">Stephan</namePart>
        <namePart type="family">Oepen</namePart>
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    <name type="personal">
        <namePart type="given">Erik</namePart>
        <namePart type="family">Velldal</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun–noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1178</identifier>
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        <date>2018-oct-nov</date>
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            <start>1488</start>
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%0 Conference Proceedings
%T Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation
%A Fares, Murhaf
%A Oepen, Stephan
%A Velldal, Erik
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F fares-etal-2018-transfer
%X In this paper, we empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task: semantic interpretation of noun–noun compounds. Through a comprehensive series of experiments and in-depth error analysis, we show that transfer learning via parameter initialization and multi-task learning via parameter sharing can help a neural classification model generalize over a highly skewed distribution of relations. Further, we demonstrate how dual annotation with two distinct sets of relations over the same set of compounds can be exploited to improve the overall accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations.
%R 10.18653/v1/D18-1178
%U https://aclanthology.org/D18-1178
%U https://doi.org/10.18653/v1/D18-1178
%P 1488-1498

Markdown (Informal)

[Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation](https://aclanthology.org/D18-1178) (Fares et al., EMNLP 2018)

• Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (Fares et al., EMNLP 2018)

ACL

• Murhaf Fares, Stephan Oepen, and Erik Velldal. 2018. Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1488–1498, Brussels, Belgium. Association for Computational Linguistics.