Address parsing models can be enhanced to handle complex and ambiguous address formats by incorporating multilingual capabilities and the ability to recognize non-standard abbreviations. One approach is to train the models on a diverse dataset that includes addresses from various countries and regions, each with its unique address format and language. This exposure will help the models learn the patterns and structures of different languages and address formats, enabling them to parse addresses accurately regardless of the language used. Additionally, integrating contextual information and semantic understanding into the models can aid in resolving ambiguities in addresses. Techniques like contextual embeddings and attention mechanisms can help the models capture the relationships between words in an address and disambiguate their meanings. Furthermore, incorporating external knowledge bases or ontologies related to addresses can provide additional context for the models to improve their parsing accuracy.