What is sequence-to-sequence modeling in NLP?

Sequence-to-sequence (seq2seq) modeling in natural language processing (NLP) refers to a neural network architecture designed to map input sequences to output sequences. It is commonly used for tasks that involve generating natural language outputs based on natural language inputs, such as machine translation, text summarization, and dialogue generation.

In a seq2seq model, the input sequence is encoded into a fixed-size representation (often referred to as the “context vector” or “thought vector”) by an encoder neural network. Then, a decoder neural network generates the output sequence based on this representation. During training, the model is trained to minimize the discrepancy between the generated output sequences and the target sequences using techniques like teacher forcing or beam search.

Seq2seq models are typically based on recurrent neural networks (RNNs), such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) networks. However, more recently, Transformer-based architectures have become popular for seq2seq tasks due to their ability to capture long-range dependencies more effectively.

Overall, seq2seq modeling has enabled significant advancements in various NLP tasks by allowing models to generate coherent and contextually relevant natural language outputs based on input sequences.