@inproceedings{mao-etal-2019-improving,
title = "Improving Neural Story Generation by Targeted Common Sense Grounding",
author = "Mao, Huanru Henry and
Majumder, Bodhisattwa Prasad and
McAuley, Julian and
Cottrell, Garrison",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1615",
doi = "10.18653/v1/D19-1615",
pages = "5988--5993",
abstract = "Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.",
}
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<abstract>Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.</abstract>
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%0 Conference Proceedings
%T Improving Neural Story Generation by Targeted Common Sense Grounding
%A Mao, Huanru Henry
%A Majumder, Bodhisattwa Prasad
%A McAuley, Julian
%A Cottrell, Garrison
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mao-etal-2019-improving
%X Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. We propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the WritingPrompts (Fan et al., 2018) story generation dataset.
%R 10.18653/v1/D19-1615
%U https://aclanthology.org/D19-1615
%U https://doi.org/10.18653/v1/D19-1615
%P 5988-5993
Markdown (Informal)
[Improving Neural Story Generation by Targeted Common Sense Grounding](https://aclanthology.org/D19-1615) (Mao et al., EMNLP-IJCNLP 2019)
ACL
- Huanru Henry Mao, Bodhisattwa Prasad Majumder, Julian McAuley, and Garrison Cottrell. 2019. Improving Neural Story Generation by Targeted Common Sense Grounding. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5988–5993, Hong Kong, China. Association for Computational Linguistics.