Technical Aspect of Translation Studies: On the Role of Python Programming Language

    Python can be used in various scenarios. As I mentioned above, just as the translation process requires a large amount of time, the evaluation process of the outputs also requires an equal amount of time. Let’s suppose that we are making an evaluation between human translation and machine translation. As we know, sometimes the findings obtained from machine translation can be inaccurate or meaningless. When an evaluation is desired at this point, Python’s flexible structure and rich library support meet this need. Thanks to Python, quality assessment analysis facilitates a comprehensive evaluation of translation output. It allows developers to identify and resolve potential problems with translation systems. Translation quality assessment analysis can be key to determining how close machine translation outputs are to human translation quality. For example, the creativity score of translation outputs can provide an assessment. A study based on lexical diversity analysis conducted by Brglez and Wintar (2024) is just one of the extensive studies conducted in this area with Python. By using Python, they obtained a creativity assessment based on lexical diversity in the Slovenian-English language pair. This study concluded that, although the diversity in neural machine translation is significant, it is not comparable to human creativity.

    Python plays a significant role in translation research as it can also analyze the emotional tone between texts translated with different methods, This is called sentiment analysis. In particular, this method allows you to measure positive or negative sentiments in your translations. Karcıoğlu and Aydın (2024) were able to conduct such a study on Twitter comments in the Turkish-English language pair by using Python. In their study, they used Python’s scikit-learn library for both base classifiers. Thus, they were able to compare and classify comments in both language pairs. Therefore, Python has once again proven to be useful in measuring sentiment analysis.

    Text similarity analysis is another feature that can help you make progress in your translation works with Python. Thanks to text processing and natural language processing libraries in Python, it is possible to measure the similarities between texts in the translation results. To put it clearly, text similarity measures how much the meaning or content of two pieces of text are the same. Text similarity can be used to find the equivalent of a text in the source language in the target language. For example, a translation model can match a sentence or word in the source text to its most appropriate equivalent in the target language based on similarity criteria. Python has a large NLP (Natural Language Processing) and machine learning ecosystem to perform such operations. Therefore, Python is a widely preferred language and platform to implement text similarity feature in translation work. In this field, Qian et al. (2019) used Python’s text similarity measurement features to provide insights into machine and human translation. By using Python, similarities between human and machine-generated outputs were calculated and the data was presented in terms of fluency and proficiency. The study found that machine-generated results exhibited significantly poorer proficiency performance compared to their human counterparts. This provided valuable insights into the nuances of both human and machine-generated translations, highlighting Python’s potential as a powerful tool for measuring translation quality, particularly in the area of ​​text similarity.

    As a result, Python emerged as an important element in the field of translation studies and offers profound benefits that improve the quality and effectiveness of translation efforts. Its powerful features, including the capacity to understand and manipulate syntax, grammar, and semantic structures, enable translators to achieve greater accuracy and fidelity in their translations. Moreover, the integration of Python into translation processes is key to allowing the exploration of various language functions, leading to more nuanced and contextually appropriate interpretations. In this form, Python remains an indispensable tool that raises the standards of translation studies.