To extend the proposed models for multi-lingual keyphrase generation, the T5 architecture could be fine-tuned on multilingual datasets, such as the mC4 corpus, which contains text in multiple languages. This would involve adapting the input format to accommodate different languages while ensuring that the model can generate keyphrases that are contextually relevant across various linguistic structures. Additionally, leveraging transfer learning techniques could allow the model to benefit from pre-trained multilingual embeddings, enhancing its ability to understand and generate keyphrases in languages with limited training data.
For multi-modal keyphrase generation, where both text and images are involved, the model could be adapted to process visual information alongside textual data. This could be achieved by integrating a vision transformer (ViT) or a convolutional neural network (CNN) to extract features from images, which would then be combined with the textual features from the T5 model. The input to the model could consist of a concatenation of the image features and the text (e.g., title and abstract), allowing the model to generate keyphrases that encapsulate the content of both modalities. This approach would enhance the model's capability to produce comprehensive keyphrases that reflect the full context of the documents, thereby improving its utility in diverse applications such as academic research and content summarization.