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        <title>Assessing Discourse Relations in Language Generation from GPT-2</title>
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        <namePart type="given">Wei-Jen</namePart>
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    <abstract>Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models. GPT-2, in particular, is suited for generation tasks given its left-to-right language modeling objective, yet the linguistic quality of its generated text has largely remain unexplored. Our work takes a step in understanding GPT-2’s outputs in terms of discourse coherence. We perform a comprehensive study on the validity of explicit discourse relations in GPT-2’s outputs under both organic generation and fine-tuned scenarios. Results show GPT-2 does not always generate text containing valid discourse relations; nevertheless, its text is more aligned with human expectation in the fine-tuned scenario. We propose a decoupled strategy to mitigate these problems and highlight the importance of explicitly modeling discourse information.</abstract>
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%0 Conference Proceedings
%T Assessing Discourse Relations in Language Generation from GPT-2
%A Ko, Wei-Jen
%A Li, Junyi Jessy
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ko-li-2020-assessing
%X Recent advances in NLP have been attributed to the emergence of large-scale pre-trained language models. GPT-2, in particular, is suited for generation tasks given its left-to-right language modeling objective, yet the linguistic quality of its generated text has largely remain unexplored. Our work takes a step in understanding GPT-2’s outputs in terms of discourse coherence. We perform a comprehensive study on the validity of explicit discourse relations in GPT-2’s outputs under both organic generation and fine-tuned scenarios. Results show GPT-2 does not always generate text containing valid discourse relations; nevertheless, its text is more aligned with human expectation in the fine-tuned scenario. We propose a decoupled strategy to mitigate these problems and highlight the importance of explicitly modeling discourse information.
%R 10.18653/v1/2020.inlg-1.8
%U https://aclanthology.org/2020.inlg-1.8
%U https://doi.org/10.18653/v1/2020.inlg-1.8
%P 52-59

Markdown (Informal)

[Assessing Discourse Relations in Language Generation from GPT-2](https://aclanthology.org/2020.inlg-1.8) (Ko & Li, INLG 2020)

• Assessing Discourse Relations in Language Generation from GPT-2 (Ko & Li, INLG 2020)

ACL

• Wei-Jen Ko and Junyi Jessy Li. 2020. Assessing Discourse Relations in Language Generation from GPT-2. In Proceedings of the 13th International Conference on Natural Language Generation, pages 52–59, Dublin, Ireland. Association for Computational Linguistics.