LSTM (Long Short Term Memory) & Dialog Systems


LSTM is a type of artificial neural network that is well-suited for processing sequential data, such as time series or natural language text. It is a variant of the RNN architecture, which is a type of ANN that is able to process sequential data by using feedback connections to process data from previous time steps.

LSTM networks are characterized by the use of “memory cells” that can store information for long periods of time, as well as “input gates”, “output gates”, and “forget gates” that control the flow of information into and out of the memory cells. This allows LSTM networks to have a longer “memory” than traditional RNNs, making them more effective at tasks that require the ability to remember past events.

LSTM networks have been successfully applied to a variety of tasks, including language translation, speech recognition, and time series prediction. They have also been used in the development of dialog systems, where they can help improve the ability of the system to understand and respond to user inputs.

LSTM networks are often used for text generation tasks because of their ability to process sequential data and retain long-term memory. In text generation, an LSTM network is typically trained on a large dataset of text, such as a collection of books or articles. The network is then able to generate new text that is similar in style and content to the training data.

To generate text, the LSTM network processes the input text one word or character at a time, using its memory cells and gates to store and retrieve information as it goes. At each time step, the network uses the stored information to predict the next word or character in the sequence. The predicted word or character is then used as input to the network at the next time step, and the process is repeated until the desired amount of text has been generated.

There are many ways in which LSTM networks can be used for text generation, including generating novel ideas for stories or articles, creating realistic dialogue for virtual assistants or chatbots, and even generating music or art. The quality and style of the generated text will depend on the quality and diversity of the training data, as well as the design of the LSTM network itself.



See also:

100 Best Convolutional Neural Network Videos | 100 Best Java Neural Network Videos | 100 Best MATLAB Neural Network Videos | 100 Best Neural Network Training Videos | 100 Best Neural Network Tutorial Videos | 100 Best Recurrent Neural Network Videos | CNN (Convolutional Neural Network) & Dialog Systems 2016 | DNN (Deep Neural Network) & Human Language Technology 2017 | Natural Language Generation, Deep Neural Networks & Dialog Systems 2017 | Neural Conversation Models 2016 | Neural Dialog Models | Neural Dialog Systems | Neural Language Models 2016 | Neural Network & Dialog Systems 2016 | Neural Question Generation 2017 | Neural Summarization | Neural Turing Machines 2016 | NMT (Neural Machine Translation) & Dialog Systems 2016 | NSCA (Neural-Symbolic Cognitive Agent) | PNN (Probabilistic Neural Network) & Dialog Systems | RNN (Recurrent Neural Network) & Dialog Systems 2016 | RNN (Recurrent Neural Network) & Question Answering Systems 2016