  The domain of Botany is rich with metaphorical terms. Those terms play an
important role in the description and identification of flowers and plants.
However, the identification of such terms in discourse is an arduous task. This
leads in some cases to committing errors during translation processes and
lexicographic tasks. The process is even more challenging when it comes to
machine translation, both in the cases of single-word terms and multi-word
terms. One of the recent concerns of Natural Language Processing (NLP)
applications and Machine Translation (MT) technologies is the automatic
identification of metaphor-based words in discourse through Deep Learning (DL).
In this study, we seek to fill this gap through the use of thirteen popular
transformer based models, as well as ChatGPT, and we show that discriminative
models perform better than GPT-3.5 model with our best performer reporting
92.2349% F1 score in metaphoric flower and plant names identification task.
