Given three words of a sentence, it should predict next word. I am using LSTM (Long-Short Term Memory) here.
- LSTM stands for Long-Short Term Memory and it is type of recurrent neural network(RNN) architecture that is specially designed to overcome the problem of vanishing gradients in traditional RNNs.
- LSTM networks are particularly well-suited to tasks that require modeling long-term dependencies between input sequences, such as language modeling, speech recognition, and machine translation.
- In an LSTM network, each cell has a "memory" component that can selectively retain or forget information over time.
- The network uses input, output, and forget gates to control the flow of information into and out of the memory cell.
- These gates can be thought of as filters that decide what information should be retained or discarded based on the current input and the previous state of the network.
- LSTM can be used for next word prediction in natural language processing (NLP) tasks.
- In this context, the LSTM network is trained on a large corpus of text to learn the patterns and relationships between words in a sentence or a document.
- At each time step, the LSTM takes in a sequence of words as input and predicts the probability distribution over the vocabulary of possible next words.
- This probability distribution is then used to sample the next word, which becomes the input for the next time step.
- The advantage of using LSTM for next word prediction is that it can capture long-term dependencies between words and phrases in the input sequence.
- This means that the network can use information from earlier parts of the sequence to make more accurate predictions about the next word.
- Overall, LSTM can help improve the accuracy and fluency of next word prediction in NLP tasks by leveraging its ability to model long-term dependencies and selectively retain or forget information from the input sequence.