Incorporating additional information, such as linguistic features or contextual cues, into the subword tokenization process could help mitigate the negative effects of aggressive vocabulary trimming. By leveraging linguistic knowledge about word structures, morphological patterns, or syntactic information, the tokenizer can make more informed decisions about which subwords to retain or discard. Contextual information from the surrounding words or phrases can also guide the tokenization process, ensuring that important subwords are preserved for accurate representation. By integrating these supplementary details into the tokenization algorithm, the model can maintain a balance between vocabulary size reduction and linguistic richness, potentially improving overall performance in neural machine translation and other language processing tasks.