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- W3200752563 abstract "A lot of work has been done in the past to identify abstracts like humor, sarcasm, and even irony in sentences. This work tries to figure out whether the text is humorous or not by using a base, large, and base-openai-detector models of RoBERTa, which are trained on ColBERT dataset having 200 k formal texts with 100 k humorous and 100 k non-humorous text in it. This is the first time implementation of humor detection using RoBERTa and its configurations. This paper compares the performance in terms of accuracy, precision, F1 score of the models of RoBERTa by taking AdamW, Adabound, and Adafactor as optimizers for the model. Experimental results show that accuracy of 99% is established by RoBERTa-large with Adafactor as its optimizer, 1% improvement compared with BERT model and 1.2% improvement compared with ALBERT models. With this, extensive experiments are done on automated loss function identification and also the comparison of Adam, Adafactor, and Adabound by varying epsilon values via cosine annealing function." @default.
- W3200752563 created "2021-09-27" @default.
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- W3200752563 date "2021-01-01" @default.
- W3200752563 modified "2023-09-27" @default.
- W3200752563 title "Automated Optimization Strategy and Usage of RoBERTa for Humor Identification" @default.
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- W3200752563 doi "https://doi.org/10.1007/978-981-16-4149-7_55" @default.
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