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        <title>Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts</title>
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    <name type="personal">
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            <namePart type="given">Emiel</namePart>
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    <abstract>The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.</abstract>
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%0 Conference Proceedings
%T Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts
%A Groves, Isabel
%A Tian, Ye
%A Douratsos, Ioannis
%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 groves-etal-2018-treat
%X The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.
%R 10.18653/v1/W18-6512
%U https://aclanthology.org/W18-6512
%U https://doi.org/10.18653/v1/W18-6512
%P 109-118

Markdown (Informal)

[Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts](https://aclanthology.org/W18-6512) (Groves et al., INLG 2018)

• Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts (Groves et al., INLG 2018)

ACL

• Isabel Groves, Ye Tian, and Ioannis Douratsos. 2018. Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts. In Proceedings of the 11th International Conference on Natural Language Generation, pages 109–118, Tilburg University, The Netherlands. Association for Computational Linguistics.