How do you choose between RNN and LSTM for natural language processing tasks?

Recurrent Neural Networks (RNNs) are a class of neural networks that are designed to operate on sequences of data such as text, speech, and time-series data. Unlike traditional feedforward neural networks, RNNs have feedback connections that allow them to maintain a state or memory of previous inputs. The basic idea of RNNs is to process a sequence of inputs one at a time, and for each input, update the internal state of the network. The output of the network at each time step can then depend not only on the current input but also on the previous inputs and the internal state of the network. One of the key features of RNNs is that they share weights across time steps, which allows them to efficiently process sequences of arbitrary length.

RNNs struggle with capturing long-term dependencies due to vanishing gradients, while LSTMs are designed to handle this by using gated units to remember important information over longer sequences. LSTMs have a memory cell structure that allows them to retain information over time and selectively update, forget, or output information, leading to more stable memory of past inputs. Training standard RNNs can be challenging due to vanishing gradients, but LSTMs are more stable during training, allowing gradients to flow more freely. RNNs are suitable for tasks with short-term dependencies, while LSTMs are preferred for tasks requiring modeling of long-term dependencies, such as machine translation and speech recognition.

RNN brings a very new aspects with respect to older neural net. That makes it easier to use sequential data to build neural net. And that is "memory". They are very useful till date for small sequences. The drawback is that the memory mechanism lacks the capability of dumping the non useful signals. And when larger sequence appears exploding and vanishing gradient makes them very inefficient to remember older information.

The cool aspect about LSTM (Long Short-Term Memory) is that has a special ability to remember important information from the past, which can help it make better predictions about what will happen in the future. It's like having a really good memory that can remember important things from a long time ago. Here are some examples of how LSTM can be used: Text prediction: LSTM can be trained on a large corpus of text to predict the next word in a sentence or even generate entire paragraphs of text that are similar to the original training data. Speech recognition: LSTM can be used to recognize speech by processing the audio waveform in small segments and predicting the corresponding phonemes or words.

LSTM the "Long Short Term Memory" is a special kind of algorithm widely used in NLP and Forecasting, It can remember pieces of information from the past for a long and also it resolves the issue of the vanishing gradient problem of the RNN (Recurrent Neural Network). Due to long memory, it is more accurate than other algorithms like RNN.

If you are doing any form of NLP you should use attention-based networks,such as a transformer. While everything discussed about RNNs and LSTMs is accurate, they are deprecated and you should use the latest techniques and frameworks.

A few people are mentioning that attention based NNs are the only NLP solution nowadays. While transformers are great there is some evidence to show that scaling RNNs like RWKV can perform just as well and do not suffer the quadratic memory problem traditional attention mechanisms have.

RNNs and LSTMs are good for certain tasks, but the reason attention based models are popular is because they essentially create a lookup KV cache of training data. So while RWKVs (a RNN) work, they still under-perform SOTA transformers.

TLDR, Don't use RNNs or LSTMs in production. They're good to learn and understand when you're entering the world of NLP, but are in fact, riddled with a host of problems. In fact, they'll most likely fail for tasks such as machine translation and summarization. There's far more advanced and a wide, ever-increasing variety of alternatives, which have fixed some of these problems, such as VAEs, Transformer models, etc. Explore these alternatives for your specific use-case.

Transformer models like BERT and GPT are currently at the forefront of new trends in NLP, thanks to their ability to capture context, understand long-range dependencies, and exhibit remarkable versatility. A key breakthrough of transformers is their ability to capture contextual information effectively and efficiently. Unlike sequential models like RNNs and LSTMs, transformers process input data in parallel, allowing them to consider all parts of a sentence simultaneously. Moreover, transformer models employ a mechanism called "attention", which enables then to capture long-range dependencies in data often more effectively than alternatives.

Use RNN for short-term dependencies and LSTM for long-term dependencies. Sentiment analysis: RNN might be more useful for analyzing short sentences and tweets to determine sentiment, while LSTM could be better suited for analyzing longer text passages where the sentiment might change over time. Music generation: RNN could be useful for generating short musical phrases, while LSTM might be more suited for generating longer pieces of music with complex structure and dynamics. Speech recognition: RNN could be useful for recognizing short phrases and simple commands, while LSTM could be better suited for recognizing longer, more complex sentences with a wider range of vocabulary.

Although we are living in the age of transformers and LLMs but LSTM and RNN still holds there relevancy. They are still useful for signal detection and time series predictions. Apart from this they should be used for small sequence NLP tasks.