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        <title>What can we learn from Semantic Tagging?</title>
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    <name type="personal">
        <namePart type="given">Mostafa</namePart>
        <namePart type="family">Abdou</namePart>
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    <name type="personal">
        <namePart type="given">Artur</namePart>
        <namePart type="family">Kulmizev</namePart>
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    <name type="personal">
        <namePart type="given">Vinit</namePart>
        <namePart type="family">Ravishankar</namePart>
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    <name type="personal">
        <namePart type="given">Lasha</namePart>
        <namePart type="family">Abzianidze</namePart>
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    <name type="personal">
        <namePart type="given">Johan</namePart>
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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">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1526</identifier>
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%0 Conference Proceedings
%T What can we learn from Semantic Tagging?
%A Abdou, Mostafa
%A Kulmizev, Artur
%A Ravishankar, Vinit
%A Abzianidze, Lasha
%A Bos, Johan
%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 abdou-etal-2018-learn
%X We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting which shows consistent gains across all tasks.
%R 10.18653/v1/D18-1526
%U https://aclanthology.org/D18-1526
%U https://doi.org/10.18653/v1/D18-1526
%P 4881-4889

Markdown (Informal)

[What can we learn from Semantic Tagging?](https://aclanthology.org/D18-1526) (Abdou et al., EMNLP 2018)

• What can we learn from Semantic Tagging? (Abdou et al., EMNLP 2018)

ACL

• Mostafa Abdou, Artur Kulmizev, Vinit Ravishankar, Lasha Abzianidze, and Johan Bos. 2018. What can we learn from Semantic Tagging?. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4881–4889, Brussels, Belgium. Association for Computational Linguistics.