次の段落に基づいて質問する
We propose a novel deep learning architecture that utilizes attention mechanisms to better capture long-term dependencies in sequential data. Our model, called Attention-RNN, employs gated recurrent units (GRUs) to encode the input sequence and uses multiple attention mechanisms to selectively focus on relevant parts of the input at each time step. This allows the model to effectively capture both short-term and long-term dependencies in the data, leading to improved performance on a variety of natural language processing tasks, including language modeling and machine translation.
1. What is the name of the proposed deep learning architecture?
2. Which recurrent units are used in the Attention-RNN model?
3. What does the Attention-RNN model utilize to better capture long-term dependencies in sequential data?
4. How does the Attention-RNN model selectively focus on relevant parts of the input at each time step?
5. What natural language processing tasks does the Attention-RNN model show improved performance on?