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In conclusion, the future trends in NLP with Deep Learning are moving towards leveraging unsupervised or unlabeled data for pre-training models and using large models like GPT-2 for zero-shot learning on a variety of tasks. By utilizing different strategies such as self-training, word vector mapping, and cross-lingual learning, researchers are making strides in improving NLP models without the need for large amounts of labeled data.

As technology continues to advance, we can expect to see even more breakthroughs in NLP with Deep Learning. The possibilities are endless and it will be exciting to see how these trends shape the future of natural language processing. Thank you to Kevin Clarke for sharing his insights and knowledge on this topic.

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