Learning to Rank Generated Portmanteaus

Authors: Lara Pollet, Thomas Winters, Pieter Delobelle
Publication Date: 2021-09-15
Publication Venue: Proceedings of the Twelfth International Conference on Computational Creativity
Abstract: Portmanteaus are a type of neologism combining two source words, for example brunch (from breakfast and lunch), and are popular for naming all kinds of phenomena. While coming up with suitable portmanteaus is a difficult creative endeavor, several portmanteau generators already exist for assistance in this process. When using these systems, it is often hard to find out which of the generated portmanteau is likely to be the best, and consequently also hard to automatically compare the quality of different portmanteau generators. In this paper, we create a model that can rank portmanteaus for two given source words, which thus aims to help find the best portmanteau to help further improve portmanteau generators. Our model first uses XGBoost trained on unlabeled generated outputs and existing portmanteaus to learn to rank portmanteaus and shows that this already greatly improves the performance of the initial generator. By ranking outputs of a state-of-the-art generator and a new simple portmanteau generator, we show by validating its quality in a human evaluation that the ranker can help visually identify the better generator, thus providing an alternative to only calculating real portmanteau generation frequency. Additionally, we find that this first model performs almost as well as a model trained on more fine-grained human-labeled portmanteaus. This indicates that just using generated and real portmanteaus is enough to create a ranker that can in turn improve the quality of the initial generator, and could additionally be of use in comparing different portmanteau generators.

Citation

APA

Pollet, L., Winters, T., & Delobelle, P. (2021). Learning to Rank Generated Portmanteaus. Proceedings of the Twelfth International Conference on Computational Creativity, 386–390.

Harvard

Pollet, L., Winters, T. and Delobelle, P. (2021) “Learning to Rank Generated Portmanteaus,” in Proceedings of the Twelfth International Conference on Computational Creativity. Association for Computational Creativity (ACC), pp. 386–390.

Vancouver

Pollet L, Winters T, Delobelle P. Learning to Rank Generated Portmanteaus. In: Proceedings of the Twelfth International Conference on Computational Creativity. Association for Computational Creativity (ACC); 2021. p. 386–90.

BibTeX

@inproceedings{pollet2021portmanteauranking,
    author = {Pollet, Lara and Winters, Thomas and Delobelle, Pieter},
    booktitle = {Proceedings of the Twelfth International Conference on Computational Creativity},
    organization = {Mexico City, Mexico (Virtual)},
    pages = {386--390},
    publisher = {Association for Computational Creativity (ACC)},
    title = {Learning to Rank Generated Portmanteaus},
    year = {2021},
    abstract = {Portmanteaus are a type of neologism combining two source words, for example brunch (from breakfast and lunch), and are popular for naming all kinds of phenomena. While coming up with suitable portmanteaus is a difficult creative endeavor, several portmanteau generators already exist for assistance in this process. When using these systems, it is often hard to find out which of the generated portmanteau is likely to be the best, and consequently also hard to automatically compare the quality of different portmanteau generators. In this paper, we create a model that can rank portmanteaus for two given source words, which thus aims to help find the best portmanteau to help further improve portmanteau generators. Our model first uses XGBoost trained on unlabeled generated outputs and existing portmanteaus to learn to rank portmanteaus and shows that this already greatly improves the performance of the initial generator. By ranking outputs of a state-of-the-art generator and a new simple portmanteau generator, we show by validating its quality in a human evaluation that the ranker can help visually identify the better generator, thus providing an alternative to only calculating real portmanteau generation frequency. Additionally, we find that this first model performs almost as well as a model trained on more fine-grained human-labeled portmanteaus. This indicates that just using generated and real portmanteaus is enough to create a ranker that can in turn improve the quality of the initial generator, and could additionally be of use in comparing different portmanteau generators.},
    isbn = {978-989-54160-3-5},
    conference = {International Conference on Computational Creativity},
}

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