Inter-sentential translation and language perspective in Neural Machine Translation: insights from ChatGPT as a transformer-based model

This study examines the challenges and advancements in inter-sentential translation and language perspective adaptation within Neural Machine Translation (NMT) systems, using ChatGPT as a representative of transformer-based models. Earlier NMT approaches, including Statistical Machine Translation (SMT) and initial neural architectures, often failed to maintain coherence across sentences or adapt cultural and linguistic nuances, leading to contextually inadequate translations. Leveraging self-attention mechanisms, ChatGPT demonstrates significant improvements, achieving 100% accuracy in resolving inter-sentential dependencies, such as gendered pronoun disambiguation, and effectively managing language perspective shifts by capturing intricate cultural, stylistic, and syntactic elements. Focusing on Indonesian-to-English translations, this study highlights the model’s ability to bridge linguistic and cultural disparities, offering a deeper understanding of language perspective. The findings highlight the importance of integrating contextual datasets and gender-balanced training materials to enhance translation quality and fairness. While ChatGPT exemplifies the potential of advanced NMT systems, its performance represents a case study and may not be generalizable to all NMT models. This research advances the discourse on NMT by emphasizing inter-sentential translation and language perspective as critical components for achieving human-like translation capabilities across diverse linguistic and cultural scenarios.