Lectures related to Natural Language Generation

Natural Language Generation: Decoding & Training

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

Delves into coreference resolution, discussing challenges, advancements, and evaluation methods.

Natural Language Generation: Decoding Techniques and Training Challenges

Covers decoding methods and training challenges in natural language generation.

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

Sequence to Sequence Models: Overview and Applications

Covers sequence to sequence models, their architecture, applications, and the role of attention mechanisms in improving performance.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Deep Learning for NLP

Introduces deep learning concepts for NLP, covering word embeddings, RNNs, and Transformers, emphasizing self-attention and multi-headed attention.

Natural Language Processing: Understanding Transformers and Tokenization

Provides an overview of Natural Language Processing, focusing on transformers, tokenization, and self-attention mechanisms for effective language analysis and synthesis.

Model Analysis

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Introduction to Modern Natural Language Processing

Introduces the course on Modern Natural Language Processing, covering its significance, applications, challenges, and advancements in technology.

Modern NLP: Classical Language Models

Covers language models, n-gram models, smoothing techniques, and fixed-context neural models.

Deep Learning for Question Answering

Explores deep learning for question answering, analyzing neural networks and model robustness to noise.

Coreference Resolution

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

Modern NLP: A Simple Neural Classifier

Introduces the basics of modern NLP, covering word embeddings, neural models, and tasks like sequence labeling and text generation.

Transformers: Revolutionizing Attention Mechanisms in NLP

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

Modern NLP: Introduction

By Antoine Bosselut introduces Natural Language Processing and its challenges, advancements in neural models, and course goals.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Neuro-symbolic Representations: Commonsense Knowledge & Reasoning

Explores neuro-symbolic representations for understanding commonsense knowledge and reasoning, emphasizing the challenges and limitations of deep learning in natural language processing.

Classical Language Models: Foundations and Applications

Introduces classical language models, their applications, and foundational concepts like count-based modeling and evaluation metrics.