Using random embeddings in deployed NMT applications poses several risks and limitations. One major risk is the potential for random embeddings to introduce unpredictable errors or biases in the translation output. Since random embeddings lack the semantic richness and contextual understanding of pre-trained embeddings, they may lead to inaccurate or nonsensical translations, especially in complex or nuanced language contexts. Additionally, the use of random embeddings could result in inconsistent performance across different language pairs or datasets, making it challenging to ensure reliable and high-quality translations in real-world applications. Moreover, the lack of semantic coherence in random embeddings may hinder the model's ability to generalize well to unseen data, leading to suboptimal translation quality and potentially impacting user experience.