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        <title>E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language</title>
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        <namePart type="given">Henry</namePart>
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    <abstract>In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6556</identifier>
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%0 Conference Proceedings
%T E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language
%A Elder, Henry
%A Gehrmann, Sebastian
%A O’Connor, Alexander
%A Liu, Qun
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F elder-etal-2018-e2e
%X In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.
%R 10.18653/v1/W18-6556
%U https://aclanthology.org/W18-6556
%U https://doi.org/10.18653/v1/W18-6556
%P 457-462

Markdown (Informal)

[E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language](https://aclanthology.org/W18-6556) (Elder et al., INLG 2018)

• E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language (Elder et al., INLG 2018)

ACL

• Henry Elder, Sebastian Gehrmann, Alexander O’Connor, and Qun Liu. 2018. E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language. In Proceedings of the 11th International Conference on Natural Language Generation, pages 457–462, Tilburg University, The Netherlands. Association for Computational Linguistics.