The findings of this paper on BPE vocabulary trimming in neural machine translation can be extrapolated to other domains utilizing neural language models. For tasks like text generation, sentiment analysis, or speech recognition, where subword tokenization plays a crucial role, similar challenges with vocabulary size and rare subwords may arise. Understanding the impact of vocabulary trimming on model performance can provide insights into optimizing neural language models across various applications. The negative effects observed in machine translation models could potentially translate to other domains, highlighting the importance of carefully managing vocabulary size and subword representation.