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        <title>Soft Representation Learning for Sparse Transfer</title>
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        <namePart type="given">Seung-won</namePart>
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    <abstract>Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances. This paper argues that hard-parameter sharing, of hard-coding layers shared across different tasks or languages, cannot generalize well, when sharing with a loosely related task. Such case, which we call sparse transfer, might actually hurt performance, a phenomenon known as negative transfer. Our contribution is using adversarial training across tasks, to “soft-code” shared and private spaces, to avoid the shared space gets too sparse. In CLL, our proposed architecture considers another challenge of dealing with low-quality input.</abstract>
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%0 Conference Proceedings
%T Soft Representation Learning for Sparse Transfer
%A Park, Haeju
%A Yeo, Jinyoung
%A Wang, Gengyu
%A Hwang, Seung-won
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F park-etal-2019-soft
%X Transfer learning is effective for improving the performance of tasks that are related, and Multi-task learning (MTL) and Cross-lingual learning (CLL) are important instances. This paper argues that hard-parameter sharing, of hard-coding layers shared across different tasks or languages, cannot generalize well, when sharing with a loosely related task. Such case, which we call sparse transfer, might actually hurt performance, a phenomenon known as negative transfer. Our contribution is using adversarial training across tasks, to “soft-code” shared and private spaces, to avoid the shared space gets too sparse. In CLL, our proposed architecture considers another challenge of dealing with low-quality input.
%R 10.18653/v1/P19-1151
%U https://aclanthology.org/P19-1151
%U https://doi.org/10.18653/v1/P19-1151
%P 1560-1568

Markdown (Informal)

[Soft Representation Learning for Sparse Transfer](https://aclanthology.org/P19-1151) (Park et al., ACL 2019)

• Soft Representation Learning for Sparse Transfer (Park et al., ACL 2019)

ACL

• Haeju Park, Jinyoung Yeo, Gengyu Wang, and Seung-won Hwang. 2019. Soft Representation Learning for Sparse Transfer. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1560–1568, Florence, Italy. Association for Computational Linguistics.