1. BERT (Bidirectional Encoder Representations from Transformers): This model revolutionized natural language understanding by capturing the context of words in a sentence using transformer-based neural networks.

2. GPT (Generative Pre-trained Transformer): A state-of-the-art language model that uses self-supervised learning to generate coherent and contextually relevant text.

3. ELMo (Embeddings from Language Models): Introduced the concept of contextual word representations by using deep bidirectional LSTM models to create word embeddings based on context.

4. Transformer-based models for machine translation: Transformer models, such as Google’s Transformer, have shown great performance in machine translation tasks by utilizing self-attention mechanisms.

5. Context-aware sentiment analysis: Recent advancements in sentiment analysis have focused on incorporating contextual information to better understand and analyze the sentiment of a given text.

6. Multilingual language models: The development of multilingual models enables natural language understanding and processing in various languages, increasing the accessibility and reach of voice assistants and sentiment analysis.

7. Pretrained models for transfer learning: Pretrained models, such as OpenAI’s GPT-3, have allowed for transfer learning, where models trained on a large corpus of data can be fine-tuned for specific tasks, reducing the need for extensive task-specific training data.

8. Named Entity Recognition (NER): NER models have become more accurate and efficient in identifying and classifying named entities (such as person names, locations, organizations) in a given text, improving overall natural language understanding.

9. Contextual intent recognition: Traditional intent recognition focused on identifying the general intent of a user query. Contextual intent recognition has made progress in understanding the intent more accurately by considering the user’s previous queries, dialog history, and contextual information.

10. Voice assistants with enhanced dialogue capabilities: Voice assistants have improved in their ability to understand and respond to complex and multi-turn conversations, utilizing advancements in natural language understanding to provide more contextually relevant and accurate responses.