How does the LSTM architecture address the challenge of capturing long-distance dependencies in language?
The Long Short-Term Memory (LSTM) architecture is a type of recurrent neural network (RNN) that has been specifically designed to address the challenge of capturing long-distance dependencies in language. In natural language processing (NLP), long-distance dependencies refer to the relationships between words or phrases that are far apart in a sentence but are still semantically related. Traditional RNNs struggle to capture these dependencies due to the vanishing gradient problem, where the gradients diminish exponentially over time, making it difficult to propagate information over long sequences.
LSTMs were introduced by Hochreiter and Schmidhuber in 1997 as a solution to the vanishing gradient problem. They achieve this by incorporating memory cells, which allow the network to selectively remember or forget information over time. The LSTM architecture consists of three main components: the input gate, the forget gate, and the output gate.
The input gate determines how much of the new input should be stored in the memory cell. It takes the current input and the previous hidden state as inputs and passes them through a sigmoid activation function. The output of the sigmoid function determines the amount of information that will be added to the memory cell. If the output is close to 0, it means that the input will be ignored, while an output close to 1 means that the input will be fully stored.
The forget gate controls the amount of information that should be discarded from the memory cell. It takes the current input and the previous hidden state as inputs and passes them through a sigmoid activation function. The output of the sigmoid function determines the amount of information that will be forgotten from the memory cell. If the output is close to 0, it means that the memory cell will retain most of its previous content, while an output close to 1 means that the memory cell will be fully reset.
The output gate determines how much information from the memory cell should be output to the next hidden state. It takes the current input and the previous hidden state as inputs and passes them through a sigmoid activation function. The output of the sigmoid function determines the amount of information that will be passed to the next hidden state. Additionally, the memory cell is passed through a tanh activation function to squash the values between -1 and 1. The output of the tanh function is then multiplied by the output of the sigmoid function to obtain the final output.
By using these gates, LSTMs are able to selectively store, forget, and output information over long sequences, allowing them to capture long-distance dependencies in language. For example, consider the sentence "The cat, which was black, jumped over the fence." In this sentence, the word "cat" is semantically related to the word "jumped," but they are separated by several other words. An LSTM can learn to associate these words by selectively storing and propagating relevant information over time.
The LSTM architecture addresses the challenge of capturing long-distance dependencies in language by incorporating memory cells and gates that allow the network to selectively store, forget, and output information over time. This enables LSTMs to capture relationships between words or phrases that are far apart in a sentence but are still semantically related.
Other recent questions and answers regarding EITC/AI/TFF TensorFlow Fundamentals:
- What is the maximum number of steps that a RNN can memorize avoiding the vanishing gradient problem and the maximum steps that LSTM can memorize?
- Is a backpropagation neural network similar to a recurrent neural network?
- How can one use an embedding layer to automatically assign proper axes for a plot of representation of words as vectors?
- What is the purpose of max pooling in a CNN?
- How is the feature extraction process in a convolutional neural network (CNN) applied to image recognition?
- Is it necessary to use an asynchronous learning function for machine learning models running in TensorFlow.js?
- What is the TensorFlow Keras Tokenizer API maximum number of words parameter?
- Can TensorFlow Keras Tokenizer API be used to find most frequent words?
- What is TOCO?
- What is the relationship between a number of epochs in a machine learning model and the accuracy of prediction from running the model?
View more questions and answers in EITC/AI/TFF TensorFlow Fundamentals