Long Short-Term Memory (LSTM)
- An RNN architecture designed to mitigate the vanishing gradient problem so networks can retain information across long sequences.
- Uses a cell state as a memory unit, controlled by input, forget, and output gates to regulate information flow.
- Commonly applied in tasks such as language modeling and machine translation.
Definition
Section titled “Definition”Long short-term memory (LSTM) is a type of recurrent neural network (RNN) architecture specifically designed to address the vanishing gradient problem in RNNs.
Explanation
Section titled “Explanation”The vanishing gradient problem occurs when a network cannot remember information from long sequences because the influence of earlier inputs decreases over time, degrading performance on tasks like language modeling and machine translation. LSTMs address this by introducing a cell state that serves as a memory unit capable of storing information over long periods. Three gates—the input gate, forget gate, and output gate—control the flow of information into and out of the cell state, thereby regulating what is retained, updated, and emitted.
Examples
Section titled “Examples”Language modeling
Section titled “Language modeling”The network is trained to predict the next word in a sentence based on the previous words. In this case, the cell state stores information about the context of the sentence, allowing the network to generate more coherent and accurate predictions.
Machine translation
Section titled “Machine translation”LSTMs can be used to translate a sentence from one language to another by storing information about the source sentence in the cell state and using it to generate a coherent translation in the target language.
Use cases
Section titled “Use cases”- Language modeling
- Machine translation
- General natural language processing tasks
Notes or pitfalls
Section titled “Notes or pitfalls”- The vanishing gradient problem makes standard RNNs unable to remember information from long sequences, which leads to poor performance on sequential tasks; LSTMs are designed to overcome this issue.
- The cell state and its three controlling gates (input, forget, output) are central to how LSTMs regulate memory over time.
Related terms
Section titled “Related terms”- Recurrent neural network (RNN)
- Vanishing gradient problem
- Cell state
- Input gate
- Forget gate
- Output gate