Bilgin, MetinBozdemir, Mehmet2023-01-202023-01-202022Bilgin, M., Bozdemir, M., (2022). Generation of Python syntax of Turkish verbal expressions with Deep Learning Approaches. Selcuk University Journal of Engineering Sciences, 21 (03), 105-110.2757-8828https://hdl.handle.net/20.500.12395/44959In this study, it is aimed to automatically generate the syntactic expression in the python programming language from Turkish verbal expressions. The study was carried out, especially in order to facilitate the learning of python code. It is aimed to provide a great convenience for teachers who give coding lessons in education-teaching environments. At the same time, it will provide the opportunity to learn coding quickly and easily for the students who take the course. A new approach has been proposed to achieve this aim. Verbally spoken sentences in Turkish are first translated into text. The transcription process was carried out using the "speech recognition" library in the python language. Turkish sentences translated into text are translated into English using deep learning algorithms such as Long Short Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). The Encoder-Decoder model, which includes the Sequence to sequence (Seq2seq) architecture, was used for machine translation. The EncoderDecoder model converts the sentences it receives in Turkish at the input to English at the output. The sentences translated into English are then given as input to the Generative Pre-trained Transformer-3 (GPT-3) model. The GPT-3 model is a pre-trained language model made by OpenAI. The GPT3 model generates the python code by taking the English sentence that comes to itself. This study is the first to produce a python code from Turkish verbal expressions. No study has been found on this. Although there is machine translation from one language to another, there is no study that finds the python code of a verbal expression in Turkish. This aspect constitutes the innovative side of our work. For the study to be used by end users, both web-based and windows-based applications were developed using the "Django" and "PyQt5" libraries. The Bilingual Evaluation Understudy (BLEU) metric was used to evaluate the results of the study. In the calculations, the BiLSTM model produced higher score values than the other modelseninfo:eu-repo/semantics/openAccessLSTMBiLSTMEncoder-DecoderGPT-3 ModelMachine TranslastionArticle213105110