Which action will resolve the problem?
Change preprocessing to use n-grams.
Add more nodes to the recurrent neural network (RNN) than the largest sentence’s word count.
Adjust hyperparameters related to the attention mechanism.
Choose a different weight initialization type.
Explanations:
Using n-grams in preprocessing could help with certain types of text processing but does not directly address the issue of translation quality in longer sentences. The seq2seq model relies on the sequence structure of the sentences rather than on n-grams.
Adding more nodes to the RNN does not necessarily improve translation quality for longer sentences. Instead, it could lead to overfitting or increased complexity without addressing the core issues of context retention and attention in longer sequences.
Adjusting hyperparameters related to the attention mechanism can improve the model’s ability to focus on relevant parts of longer sentences, thereby enhancing translation quality. Attention mechanisms are designed to help models manage long sequences by allowing them to concentrate on specific words when making predictions.
Changing the weight initialization type is unlikely to resolve the translation quality issue in longer sentences. While weight initialization can affect training dynamics, it does not directly influence how the model handles longer input sequences during translation.
I gauge that the answer is:
Adjust hyperparameters related to the attention mechanism.
In my experience, the answer is:
Adjust hyperparameters related to the attention mechanism.