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        <title>Multi-Label Transfer Learning for Multi-Relational Semantic Similarity</title>
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            <namePart type="given">Ekaterina</namePart>
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            <namePart type="given">Soujanya</namePart>
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    <abstract>Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.</abstract>
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%0 Conference Proceedings
%T Multi-Label Transfer Learning for Multi-Relational Semantic Similarity
%A Zhang, Li
%A Wilson, Steven
%A Mihalcea, Rada
%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 zhang-etal-2019-multi
%X Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.
%R 10.18653/v1/S19-1005
%U https://aclanthology.org/S19-1005
%U https://doi.org/10.18653/v1/S19-1005
%P 44-50

Markdown (Informal)

[Multi-Label Transfer Learning for Multi-Relational Semantic Similarity](https://aclanthology.org/S19-1005) (Zhang et al., *SEM 2019)

• Multi-Label Transfer Learning for Multi-Relational Semantic Similarity (Zhang et al., *SEM 2019)

ACL

• Li Zhang, Steven Wilson, and Rada Mihalcea. 2019. Multi-Label Transfer Learning for Multi-Relational Semantic Similarity. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 44–50, Minneapolis, Minnesota. Association for Computational Linguistics.