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Related Questions
- What are the key components of the encoder-decoder architecture in NLP models?
- How does the encoder-decoder architecture handle input and output sequences in machine translation and text summarization tasks?
- What are the advantages of using the encoder-decoder architecture in NLP tasks compared to other architectures?
- How does the encoder-decoder architecture handle out-of-vocabulary words in machine translation tasks?
- Can you explain the concept of attention mechanism in the encoder-decoder architecture and its role in NLP tasks?
- How does the encoder-decoder architecture handle long-range dependencies in language inputs, such as those found in text summarization tasks?
- What are some common challenges in implementing the encoder-decoder architecture in real-world NLP applications, and how are they addressed?
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