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        <title>Self-Attention Architectures for Answer-Agnostic Neural Question Generation</title>
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        <namePart type="given">Thomas</namePart>
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    <name type="personal">
        <namePart type="given">Jacopo</namePart>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks. We explore how Transformers can be adapted to the task of Neural Question Generation without constraining the model to focus on a specific answer passage. We study the effect of several strategies to deal with out-of-vocabulary words such as copy mechanisms, placeholders, and contextual word embeddings. We report improvements obtained over the state-of-the-art on the SQuAD dataset according to automated metrics (BLEU, ROUGE), as well as qualitative human assessments of the system outputs.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1604</identifier>
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%0 Conference Proceedings
%T Self-Attention Architectures for Answer-Agnostic Neural Question Generation
%A Scialom, Thomas
%A Piwowarski, Benjamin
%A Staiano, Jacopo
%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 scialom-etal-2019-self
%X Neural architectures based on self-attention, such as Transformers, recently attracted interest from the research community, and obtained significant improvements over the state of the art in several tasks. We explore how Transformers can be adapted to the task of Neural Question Generation without constraining the model to focus on a specific answer passage. We study the effect of several strategies to deal with out-of-vocabulary words such as copy mechanisms, placeholders, and contextual word embeddings. We report improvements obtained over the state-of-the-art on the SQuAD dataset according to automated metrics (BLEU, ROUGE), as well as qualitative human assessments of the system outputs.
%R 10.18653/v1/P19-1604
%U https://aclanthology.org/P19-1604
%U https://doi.org/10.18653/v1/P19-1604
%P 6027-6032

Markdown (Informal)

[Self-Attention Architectures for Answer-Agnostic Neural Question Generation](https://aclanthology.org/P19-1604) (Scialom et al., ACL 2019)

• Self-Attention Architectures for Answer-Agnostic Neural Question Generation (Scialom et al., ACL 2019)

ACL

• Thomas Scialom, Benjamin Piwowarski, and Jacopo Staiano. 2019. Self-Attention Architectures for Answer-Agnostic Neural Question Generation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6027–6032, Florence, Italy. Association for Computational Linguistics.