@inproceedings{wockener-etal-2021-end,
title = "End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?",
author = {W{\"o}ckener, J{\"o}rg and
Haider, Thomas and
Miller, Tristan and
Nguyen, The-Khang and
Nguyen, Thanh Tung Linh and
Pham, Minh Vu and
Belouadi, Jonas and
Eger, Steffen},
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.latechclfl-1.7/",
doi = "10.18653/v1/2021.latechclfl-1.7",
pages = "57--66",
abstract = "In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the {\textquoteleft}meaning' of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits low-level statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone."
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<abstract>In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the ‘meaning’ of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits low-level statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.</abstract>
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%0 Conference Proceedings
%T End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?
%A Wöckener, Jörg
%A Haider, Thomas
%A Miller, Tristan
%A Nguyen, The-Khang
%A Nguyen, Thanh Tung Linh
%A Pham, Minh Vu
%A Belouadi, Jonas
%A Eger, Steffen
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic (online)
%F wockener-etal-2021-end
%X In this work, we design an end-to-end model for poetry generation based on conditioned recurrent neural network (RNN) language models whose goal is to learn stylistic features (poem length, sentiment, alliteration, and rhyming) from examples alone. We show this model successfully learns the ‘meaning’ of length and sentiment, as we can control it to generate longer or shorter as well as more positive or more negative poems. However, the model does not grasp sound phenomena like alliteration and rhyming, but instead exploits low-level statistical cues. Possible reasons include the size of the training data, the relatively low frequency and difficulty of these sublexical phenomena as well as model biases. We show that more recent GPT-2 models also have problems learning sublexical phenomena such as rhyming from examples alone.
%R 10.18653/v1/2021.latechclfl-1.7
%U https://aclanthology.org/2021.latechclfl-1.7/
%U https://doi.org/10.18653/v1/2021.latechclfl-1.7
%P 57-66
Markdown (Informal)
[End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?](https://aclanthology.org/2021.latechclfl-1.7/) (Wöckener et al., LaTeCHCLfL 2021)
ACL
- Jörg Wöckener, Thomas Haider, Tristan Miller, The-Khang Nguyen, Thanh Tung Linh Nguyen, Minh Vu Pham, Jonas Belouadi, and Steffen Eger. 2021. End-to-end style-conditioned poetry generation: What does it take to learn from examples alone?. In Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 57–66, Punta Cana, Dominican Republic (online). Association for Computational Linguistics.