Explores coreference resolution models, challenges in scoring spans, graph refinement techniques, state-of-the-art results, and the impact of pretrained Transformers.
Explores chemical reaction prediction using generative models and molecular transformers, emphasizing the importance of molecular language processing and stereochemistry.
Explores the Transformer model, from recurrent models to attention-based NLP, highlighting its key components and significant results in machine translation and document generation.
Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.