Detecting Honkadori based on Waka Embeddings

Hayato Ogawa; Kaito Horio; Daisuke Kawahara

doi:10.18653/v1/2025.alp-1.14

Detecting Honkadori based on Waka Embeddings

Hayato Ogawa, Kaito Horio, Daisuke Kawahara

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Abstract

We develop an embedding model specifically designed for Waka poetry and use it to build a model for detecting Honkadori. Waka is a tradi-tional form of old Japanese poetry that has been composed since ancient times. Honkadori is a sophisticated poetic technique in Japanese clas-sical literature where poets incorporate words or poetic sentiments from old Wakas (Honka) into their own work. First, we fine-tune a pre-trained language model using contrastive learn-ing to construct a Waka-specialized embedding model. Then, using the embedding vectors ob-tained from this model and features extracted from them, we train a machine learning model to detect the Honka (original poem) of Wakas that employ the Honkadori technique. Using paired data of Honka and Wakas that are consid-ered to use Honkadori, we evaluated the Honka detection model and demonstrated that it can detect Honka with reasonable accuracy.

Anthology ID:: 2025.alp-1.14
Volume:: Proceedings of the Second Workshop on Ancient Language Processing
Month:: May
Year:: 2025
Address:: The Albuquerque Convention Center, Laguna
Editors:: Adam Anderson, Shai Gordin, Bin Li, Yudong Liu, Marco C. Passarotti, Rachele Sprugnoli
Venues:: ALP | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 112–119
Language:
URL:: https://aclanthology.org/2025.alp-1.14/
DOI:: 10.18653/v1/2025.alp-1.14
Bibkey:
Cite (ACL):: Hayato Ogawa, Kaito Horio, and Daisuke Kawahara. 2025. Detecting Honkadori based on Waka Embeddings. In Proceedings of the Second Workshop on Ancient Language Processing, pages 112–119, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.
Cite (Informal):: Detecting Honkadori based on Waka Embeddings (Ogawa et al., ALP 2025)
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PDF:: https://aclanthology.org/2025.alp-1.14.pdf

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@inproceedings{ogawa-etal-2025-detecting,
    title = "Detecting Honkadori based on {W}aka Embeddings",
    author = "Ogawa, Hayato  and
      Horio, Kaito  and
      Kawahara, Daisuke",
    editor = "Anderson, Adam  and
      Gordin, Shai  and
      Li, Bin  and
      Liu, Yudong  and
      Passarotti, Marco C.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the Second Workshop on Ancient Language Processing",
    month = may,
    year = "2025",
    address = "The Albuquerque Convention Center, Laguna",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.alp-1.14/",
    doi = "10.18653/v1/2025.alp-1.14",
    pages = "112--119",
    ISBN = "979-8-89176-235-0",
    abstract = "We develop an embedding model specifically designed for Waka poetry and use it to build a model for detecting Honkadori. Waka is a tradi-tional form of old Japanese poetry that has been composed since ancient times. Honkadori is a sophisticated poetic technique in Japanese clas-sical literature where poets incorporate words or poetic sentiments from old Wakas (Honka) into their own work. First, we fine-tune a pre-trained language model using contrastive learn-ing to construct a Waka-specialized embedding model. Then, using the embedding vectors ob-tained from this model and features extracted from them, we train a machine learning model to detect the Honka (original poem) of Wakas that employ the Honkadori technique. Using paired data of Honka and Wakas that are consid-ered to use Honkadori, we evaluated the Honka detection model and demonstrated that it can detect Honka with reasonable accuracy."
}

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%0 Conference Proceedings
%T Detecting Honkadori based on Waka Embeddings
%A Ogawa, Hayato
%A Horio, Kaito
%A Kawahara, Daisuke
%Y Anderson, Adam
%Y Gordin, Shai
%Y Li, Bin
%Y Liu, Yudong
%Y Passarotti, Marco C.
%Y Sprugnoli, Rachele
%S Proceedings of the Second Workshop on Ancient Language Processing
%D 2025
%8 May
%I Association for Computational Linguistics
%C The Albuquerque Convention Center, Laguna
%@ 979-8-89176-235-0
%F ogawa-etal-2025-detecting
%X We develop an embedding model specifically designed for Waka poetry and use it to build a model for detecting Honkadori. Waka is a tradi-tional form of old Japanese poetry that has been composed since ancient times. Honkadori is a sophisticated poetic technique in Japanese clas-sical literature where poets incorporate words or poetic sentiments from old Wakas (Honka) into their own work. First, we fine-tune a pre-trained language model using contrastive learn-ing to construct a Waka-specialized embedding model. Then, using the embedding vectors ob-tained from this model and features extracted from them, we train a machine learning model to detect the Honka (original poem) of Wakas that employ the Honkadori technique. Using paired data of Honka and Wakas that are consid-ered to use Honkadori, we evaluated the Honka detection model and demonstrated that it can detect Honka with reasonable accuracy.
%R 10.18653/v1/2025.alp-1.14
%U https://aclanthology.org/2025.alp-1.14/
%U https://doi.org/10.18653/v1/2025.alp-1.14
%P 112-119

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Markdown (Informal)

[Detecting Honkadori based on Waka Embeddings](https://aclanthology.org/2025.alp-1.14/) (Ogawa et al., ALP 2025)

Detecting Honkadori based on Waka Embeddings (Ogawa et al., ALP 2025)

ACL

Hayato Ogawa, Kaito Horio, and Daisuke Kawahara. 2025. Detecting Honkadori based on Waka Embeddings. In Proceedings of the Second Workshop on Ancient Language Processing, pages 112–119, The Albuquerque Convention Center, Laguna. Association for Computational Linguistics.