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    <titleInfo>
        <title>Transfer Learning Parallel Metaphor using Bilingual Embeddings</title>
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    <name type="personal">
        <namePart type="given">Maria</namePart>
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            <title>Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)</title>
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        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
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        <name type="personal">
            <namePart type="given">Soujanya</namePart>
            <namePart type="family">Poria</namePart>
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        <name type="personal">
            <namePart type="given">Tuhin</namePart>
            <namePart type="family">Chakrabarty</namePart>
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    <abstract>Automated metaphor detection in languages other than English is highly restricted as training corpora are comparably rare. One way to overcome this problem is transfer learning. This paper gives an overview on transfer learning techniques applied to NLP. We first introduce types of transfer learning, then we present work focusing on: i) transfer learning with cross-lingual embeddings; ii) transfer learning in machine translation; and iii) transfer learning using pre-trained transformer models. The paper is complemented by first experiments that make use of bilingual embeddings generated from different sources of parallel data: We i) present the preparation of a parallel Gold corpus; ii) examine the embeddings spaces to search for metaphoric words cross-lingually; iii) run first experiments in transfer learning German metaphor from English labeled data only. Results show that finding data sources for bilingual embeddings training and the vocabulary covered by these embeddings is critical for learning metaphor cross-lingually.</abstract>
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    <identifier type="doi">10.18653/v1/2022.flp-1.3</identifier>
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%0 Conference Proceedings
%T Transfer Learning Parallel Metaphor using Bilingual Embeddings
%A Berger, Maria
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F berger-2022-transfer
%X Automated metaphor detection in languages other than English is highly restricted as training corpora are comparably rare. One way to overcome this problem is transfer learning. This paper gives an overview on transfer learning techniques applied to NLP. We first introduce types of transfer learning, then we present work focusing on: i) transfer learning with cross-lingual embeddings; ii) transfer learning in machine translation; and iii) transfer learning using pre-trained transformer models. The paper is complemented by first experiments that make use of bilingual embeddings generated from different sources of parallel data: We i) present the preparation of a parallel Gold corpus; ii) examine the embeddings spaces to search for metaphoric words cross-lingually; iii) run first experiments in transfer learning German metaphor from English labeled data only. Results show that finding data sources for bilingual embeddings training and the vocabulary covered by these embeddings is critical for learning metaphor cross-lingually.
%R 10.18653/v1/2022.flp-1.3
%U https://aclanthology.org/2022.flp-1.3
%U https://doi.org/10.18653/v1/2022.flp-1.3
%P 13-23

Markdown (Informal)

[Transfer Learning Parallel Metaphor using Bilingual Embeddings](https://aclanthology.org/2022.flp-1.3) (Berger, Fig-Lang 2022)

• Transfer Learning Parallel Metaphor using Bilingual Embeddings (Berger, Fig-Lang 2022)

ACL

• Maria Berger. 2022. Transfer Learning Parallel Metaphor using Bilingual Embeddings. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 13–23, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.