Natural Language Generation: Understanding the Task and Techniques

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Page 2 of 4

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Coreference Resolution

Covers coreference resolution, models, applications, challenges, and advancements in natural language processing.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Transformer: Pre-Training

Explores the Transformer model, from recurrent models to attention-based NLP, highlighting its key components and significant results in machine translation and document generation.

Natural Language Generation

Explores Natural Language Generation, covering neural models, biases, ethics, and evaluation challenges.

Neural Machine Translation

Explores the evolution and challenges of Neural Machine Translation systems and the evaluation metrics used in this field.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Machine Translation: Sequence-to-Sequence and Attention

Explores the advancements in Machine Translation, focusing on Sequence-to-Sequence models and Attention mechanisms.

Natural Language Generation: Task

On NLG covers basics, autoregressive models, decoding methods, and challenges in text generation.