From Sentence Embeddings to Large Language Models to Detect and Understand Wordplay

Author:

Dsilva Ryan Rony

Publisher

Springer Nature Switzerland

Reference14 articles.

1. Annamoradnejad, I., Zoghi, G.: ColBERT: using BERT sentence embedding in parallel neural networks for computational humor. Expert Syst. Appl. 249, 123685 (2024)

2. Bai, J., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)

3. Chang, Y., et al.: A survey on evaluation of large language models. ACM Trans. Intell. Syst. Technol. 15(3), 1–45 (2024)

4. Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451 (2020)

5. Dsilva, R.R.: AKRaNLU@ CLEF JOKER 2023: using sentence embeddings and multilingual models to detect and interpret wordplay. In: Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2023), Thessaloniki, Greece, 18–21 September 2023. CEUR Workshop Proceedings, vol. 3497, pp. 1846–1853 (2023). https://ceur-ws.org/Vol-3497/paper-154.pdf

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