@inproceedings{delucia-etal-2021-decoding,
title = "Decoding Methods for Neural Narrative Generation",
author = "DeLucia, Alexandra and
Mueller, Aaron and
Li, Xiang Lisa and
Sedoc, Jo{\~a}o",
editor = "Bosselut, Antoine and
Durmus, Esin and
Gangal, Varun Prashant and
Gehrmann, Sebastian and
Jernite, Yacine and
Perez-Beltrachini, Laura and
Shaikh, Samira and
Xu, Wei",
booktitle = "Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.gem-1.16",
doi = "10.18653/v1/2021.gem-1.16",
pages = "166--185",
abstract = "Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation. In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters{---}specifically, maximum mutual information{---}analyzing results over multiple criteria with automatic and human evaluation. We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.",
}
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%0 Conference Proceedings
%T Decoding Methods for Neural Narrative Generation
%A DeLucia, Alexandra
%A Mueller, Aaron
%A Li, Xiang Lisa
%A Sedoc, João
%Y Bosselut, Antoine
%Y Durmus, Esin
%Y Gangal, Varun Prashant
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Perez-Beltrachini, Laura
%Y Shaikh, Samira
%Y Xu, Wei
%S Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F delucia-etal-2021-decoding
%X Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances in decoding methods for neural response generation to neural narrative generation. In particular, we employ GPT-2 and perform ablations across nucleus sampling thresholds and diverse decoding hyperparameters—specifically, maximum mutual information—analyzing results over multiple criteria with automatic and human evaluation. We find that (1) nucleus sampling is generally best with thresholds between 0.7 and 0.9; (2) a maximum mutual information objective can improve the quality of generated stories; and (3) established automatic metrics do not correlate well with human judgments of narrative quality on any qualitative metric.
%R 10.18653/v1/2021.gem-1.16
%U https://aclanthology.org/2021.gem-1.16
%U https://doi.org/10.18653/v1/2021.gem-1.16
%P 166-185
Markdown (Informal)
[Decoding Methods for Neural Narrative Generation](https://aclanthology.org/2021.gem-1.16) (DeLucia et al., GEM 2021)
ACL
- Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li, and João Sedoc. 2021. Decoding Methods for Neural Narrative Generation. In Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021), pages 166–185, Online. Association for Computational Linguistics.