A Neural Machine Translation Framework Utilizing Attention Mechanisms for Multilingual Date Translation

Date translation across multiple languages and formats remains an important challenge, as it is influenced by the forms of representation that vary in linguistics variations and the nuances of cultures. Although NMT has achieved major success regarding general text translations, domain-specific tasks, such as the translation task of dates across multiple languages, are relatively unexplored. We have introduced a novel approach for multilingual date translation that relies on an NMT model with an augmented attention mechanism that captures contextual dependencies by using a sequence-to-sequence architecture with a pre-attention Bi-LSTM layer. Then an attention mechanism is applied in the sequence to focus on the right parts, and the post-attention LSTM generates the translated date in the desired format. Our dataset contains various date formats, namely French, German, Spanish, Chinese, and Russian, which pose different structural and linguistic challenges. The model was trained to standardize all dates to the format of ISO 8601 (YYYY-MM-DD). We find our experiments to demonstrate a few significant areas of improvement in terms of translation accuracy and consistency, particularly format variation and language-specific date representations. Further, our work contributes toward robust multilingual NMT models, filling the gap in translating structured entities like dates and improving the understanding of applications of attention mechanisms in domain-specific tasks.