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        <title>Scalable Cross-Lingual Transfer of Neural Sentence Embeddings</title>
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            <title>Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)</title>
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            <namePart type="given">Rada</namePart>
            <namePart type="family">Mihalcea</namePart>
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        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
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        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
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        <name type="personal">
            <namePart type="given">Soujanya</namePart>
            <namePart type="family">Poria</namePart>
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    <abstract>We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.</abstract>
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    <identifier type="doi">10.18653/v1/S19-1006</identifier>
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%0 Conference Proceedings
%T Scalable Cross-Lingual Transfer of Neural Sentence Embeddings
%A Aldarmaki, Hanan
%A Diab, Mona
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F aldarmaki-diab-2019-scalable
%X We develop and investigate several cross-lingual alignment approaches for neural sentence embedding models, such as the supervised inference classifier, InferSent, and sequential encoder-decoder models. We evaluate three alignment frameworks applied to these models: joint modeling, representation transfer learning, and sentence mapping, using parallel text to guide the alignment. Our results support representation transfer as a scalable approach for modular cross-lingual alignment of neural sentence embeddings, where we observe better performance compared to joint models in intrinsic and extrinsic evaluations, particularly with smaller sets of parallel data.
%R 10.18653/v1/S19-1006
%U https://aclanthology.org/S19-1006
%U https://doi.org/10.18653/v1/S19-1006
%P 51-60

Markdown (Informal)

[Scalable Cross-Lingual Transfer of Neural Sentence Embeddings](https://aclanthology.org/S19-1006) (Aldarmaki & Diab, *SEM 2019)

• Scalable Cross-Lingual Transfer of Neural Sentence Embeddings (Aldarmaki & Diab, *SEM 2019)

ACL

• Hanan Aldarmaki and Mona Diab. 2019. Scalable Cross-Lingual Transfer of Neural Sentence Embeddings. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 51–60, Minneapolis, Minnesota. Association for Computational Linguistics.