There are several state-of-the-art techniques in natural language processing that have emerged in recent years. Some of these include:

1. Transformer models: These are neural network models that use self-attention mechanisms to process input sequences, and have achieved state-of-the-art performance on a variety of NLP tasks.

2. Pre-trained language models: These are large neural network models that are pre-trained on massive amounts of text data and then fine-tuned for specific NLP tasks. Examples include BERT, GPT-2, and RoBERTa.

3. Transfer learning: This involves training models on one task and then transferring the knowledge learned to another related task, and has been shown to improve performance on various NLP tasks.

4. Multi-task learning: This involves training a single model on multiple NLP tasks, which can lead to improved performance on all tasks.

5. Neural machine translation: This involves training neural network models to translate between different languages, and has achieved significant improvements in translation quality over traditional statistical methods.

These are just a few examples of state-of-the-art techniques in NLP, and the field is constantly evolving with new approaches and innovations.
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