What is the Long-term Dependency Problem?
In deep learning, when handling sequential data, older information tends to get forgotten as time progresses.
This is known as the Long-term Dependency Problem.
Understanding the Long-term Dependency Problem Through Examples
When dealing with information where the sequence is important, such as sentences, speech, and time-sequenced data, earlier content can be relevant later.
Let's take a look at the following sentence as an example.
The breakfast I had this morning was really delicious, so I was in a good mood.
Here, the phrase "so I was in a good mood" is linked to the earlier information about the "delicious breakfast."
If the neural network forgets the context from earlier, it might not properly understand the significance of "so I was in a good mood."
RNNs are inherently designed to remember past information, but they tend to forget earlier information over time.
As a result, the longer the sentence, the more likely it is to lose important context and make incorrect inferences.
Why does this problem occur?
The long-term dependency problem arises due to the nature of gradients used during the learning process of RNNs.
RNNs are structured to process sequential data by passing information from one step to the next.
However, problems emerge in this information transfer as learning progresses.
Neural networks use backpropagation to reduce errors during learning.
In this process, errors are propagated backward, and gradients are calculated at each step to adjust weights.
But gradients may shrink (known as vanishing gradients) or grow larger (known as exploding gradients) as they pass through multiple steps.
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If the gradients become too small → Initial information nearly disappears and isn't transmitted.
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If the gradients become too large → Learning becomes unstable and the model may not converge properly.
For these reasons, critical information at the beginning of a sentence becomes increasingly difficult for the neural network to remember as time passes.
Thus, as sentences become longer, the early context may not be accurately reflected, making correct predictions or conclusions challenging.
How can we solve this problem?
LSTM and GRU are prominent structures developed to address this issue.
Rather than simply passing on past information, they have mechanisms for remembering important information and forgetting unnecessary information, which can effectively mitigate the long-term dependency problem.
In the next session, we'll explore a simple quiz based on what we've learned so far.
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