TEXT GENERATION MODELS FOR PARAPHRASE ON KAZAKH LANGUAGE

Abstract

This study delves into the relatively unexplored domain of natural language processing for the Kazakh language—a language with limited computational resources. The paper dissects the effectiveness of diffusion models and transformers in generating text, specifically paraphrases, which is a critical aspect of machine learning applications such as chatbots, virtual assistants, and automated translation services.
 The researchers methodically adapt these advanced models to understand and generate Kazakh text, tackling the unique challenges posed by the language's complex morphology. The paper is comprehensive in its approach, covering everything from the initial adaptation of the models to the Kazakh language context, to the creation of specialized tokenizer tools, to the translation and preparation of datasets for effective training.
 Through rigorous testing and performance analysis, the study identifies the strengths and weaknesses of each model type. This is critical as it informs the direction of future research and model development, with the goal of enhancing the fluency and accuracy of automated Kazakh text generation. The paper also discusses the broader impact of its findings, suggesting that the methodologies and insights gained could inform similar efforts in other low-resource languages, thereby contributing to the global field of NLP.
 The research concludes with reflections on the implications of their findings for the ongoing development of machine learning technologies, asserting the potential of these technologies to accommodate the intricacies of any language, given the right approach and resources. This work not only advances the technical capabilities for Kazakh text generation but also serves as a testament to the potential of machine learning to bridge language gaps and foster greater digital inclusivity.