Sequence Models in Natural Language Processing

Sequence models are a class of models that work with sequences of data, such as text, audio, and time series. In natural language processing (NLP), sequence models are particularly important for tasks like language modeling, machine translation, and speech recognition.

Key Concepts

• RNN (Recurrent Neural Network): A type of neural network that processes sequences by looping information through hidden layers.
• LSTM (Long Short-Term Memory): An advanced version of RNN that can learn long-term dependencies in sequences.
• GRU (Gated Recurrent Unit): A simpler and more efficient alternative to LSTM, which is often used in sequence models.

Applications

• Language Modeling: Predicting the next word in a sentence or text.
• Machine Translation: Translating text from one language to another.
• Speech Recognition: Converting spoken words into written text.

Resources

• Introduction to RNNs
• Understanding LSTM

RNN Architecture

Conclusion

Sequence models have revolutionized the field of NLP, enabling machines to understand and generate human language. As these models continue to evolve, we can expect even more advanced applications in the future.