The SEE model, designed for EEG-to-Text translation, can be extended to handle more complex language tasks like question answering (QA) and dialogue generation by incorporating additional modules and training strategies. For question answering, the model could integrate a retrieval-based mechanism that allows it to access a knowledge base or context-specific information. This could involve adding a context encoder that processes relevant documents or passages alongside the EEG signals, enabling the model to generate answers based on both the brain activity and the retrieved information.
For dialogue generation, the SEE model could be adapted to maintain conversational context by implementing a memory mechanism that tracks dialogue history. This would allow the model to generate contextually relevant responses based on previous interactions, enhancing the coherence and relevance of the dialogue. Additionally, training the model on dialogue datasets could help it learn the nuances of conversational language, such as turn-taking and response appropriateness.
In both cases, leveraging multi-turn interactions and incorporating reinforcement learning techniques could further refine the model's ability to generate accurate and contextually appropriate responses, thereby broadening its applicability beyond simple text generation.