This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into different modifications and enhancements applied to both standard and neural network architectures for constructing language models. Special attention is given to addressing challenges specific to agglutinative languages within the context of developing language models for various NLP tasks, particularly for Arabic and Turkish. The research highlights that contemporary transformer-based methods demonstrate results comparable to those achieved by traditional methods employing Hidden Markov Models. These transformer-based approaches boast simpler configurations and exhibit faster performance during both training and analysis. An integral component of the article is the examination of popular and actively evolving libraries and tools essential for constructing language models. Notable tools such as NLTK, TensorFlow, PyTorch, and Gensim are reviewed, with a comparative analysis considering their simplicity and accessibility for implementing diverse language models. The aim is to provide readers with insights into the landscape of contemporary language modeling methodologies and the tools available for their implementation.

Neural machine translation (NMT) plays a vital role in modern communication by bridging language barriers and enabling effective information exchange across diverse linguistic communities. Due to the limited availability of data in low-resource languages, NMT faces significant translation challenges. Data sparsity limits NMT models’ ability to learn, generalize, and produce accurate translations, which leads to low coherence and poor context awareness. This paper proposes a transformer-based approach incorporating an encoder–decoder structure, bilingual curriculum learning, and contrastive re-ranking mechanisms. Our approach enriches the training dataset using back-translation and enhances the model’s contextual learning through BERT embeddings. An incomplete-trust (in-trust) loss function is introduced to replace the traditional cross-entropy loss during training. The proposed model effectively handles out-of-vocabulary words and integrates named entity recognition techniques to maintain semantic accuracy. Additionally, the self-attention layers in the transformer architecture enhance the model’s syntactic analysis capabilities, which enables better context awareness and more accurate translations. Extensive experiments are performed on a diverse Chinese–Urdu parallel corpus, developed using human effort and publicly available datasets such as OPUS, WMT, and WiLi. The proposed model demonstrates a BLEU score improvement of 1.80% for Zh→Ur and 2.22% for Ur→Zh compared to the highest-performing comparative model. This significant enhancement indicates better translation quality and accuracy.