| Resumen |
The following work has the purpose of describing the participation in the JOKER 2023 track in the classification Task 1 in which it is asked to classify sentences that contain wordplay and the translation Task 3 in which this same type of sentence has to be translated into Spanish trying to maintain the wordplay and the sense in a certain way tropicalizing the sentence to Spanish language from English. Given the constant use of models such as GPT and T5, it was decided to take the direction of language models, making a slight fine-tuning of BLOOMZ & mT5 models with which to address the problem of translation with a simple prompt and on the other hand the use of BERT as a model proposed for the classification task. The results were mixed since in the manual review of the BLOOMZ & mT5 translated sentences not many were found to contain the Spanish pun, in the case of classification somewhat similar results were obtained given the structure of the dataset. It is believed that given their performance the models can still be optimized and improve the accuracy with which they classify, in the case of the translations perhaps another methodology could be chosen to improve the results obtained in this task, in general, the results are satisfactory for a first approach. © 2023 Copyright for this paper by its authors. |
