InfoCoBuild

CS224U - Natural Language Understanding

CS224U: Natural Language Understanding. Instructors: Prof. Christopher Potts, Prof. Bill MacCartney, and Omar Khattab, Department of Linguistics and Computer Science, Stanford University. From conversational agents to automated trading and search queries, natural language understanding underpins many of today's most exciting technologies. How do we build these models to understand language efficiently and reliably? In this project-oriented course, you will develop systems and algorithms for robust machine understanding of human language. The course draws on theoretical concepts from linguistics, natural language processing, and machine learning. You can find more information about this course, such as lecture slides and syllabus, here. (from Stanfordonline)

Lecture 27 - Grounded Language Understanding: Speakers


Go to the Course Home or watch other lectures:

Lecture 01 - Course Overview
Lecture 02 - Homework 1: Word Relatedness
Lecture 03 - Distributed Word Representations: High-Level Goals and Hypotheses
Lecture 04 - Distributed Word Representations: Matrix Designs
Lecture 05 - Distributed Word Representations: Vector Comparison
Lecture 06 - Distributed Word Representations: Basic Reweighting
Lecture 07 - Distributed Word Representations: Dimensionality Reduction
Lecture 08 - Distributed Word Representations: Retrofitting
Lecture 09 - Distributed Word Representations: Static Representations from Contextual Models
Lecture 10 - Homework 2: Sentiment Analysis
Lecture 11 - Overview of Supervised Sentiment Analysis
Lecture 12 - Supervised Sentiment Analysis: General Practical Tips
Lecture 13 - Supervised Sentiment Analysis: Stanford Sentiment Treebank
Lecture 14 - Supervised Sentiment Analysis: DynaSent
Lecture 15 - Supervised Sentiment Analysis: sst.py
Lecture 16 - Supervised Sentiment Analysis: Hyperparameter Search
Lecture 17 - Supervised Sentiment Analysis: Feature Representation
Lecture 18 - Supervised Sentiment Analysis: RNN Classifiers
Lecture 19 - Contextual Representation Models
Lecture 20 - Contextual Word Representations: Transformers
Lecture 21 - Contextual Word Representations: BERT
Lecture 22 - Contextual Word Representations: RoBERTa
Lecture 23 - Contextual Word Representations: ELECTRA
Lecture 24 - Contextual Word Representations: Practical Fine-tuning
Lecture 25 - Homework 3: Colors
Lecture 26 - Grounded Language Understanding
Lecture 27 - Grounded Language Understanding: Speakers
Lecture 28 - Grounded Language Understanding: Listeners
Lecture 29 - Grounded Language Understanding: Varieties of Contextual Grounding
Lecture 30 - Grounded Language Understanding: The Rational Speech Acts Model
Lecture 31 - Grounded Language Understanding: Neural RSA
Lecture 32 - Natural Language Inference: Overview
Lecture 33 - Natural Language Inference: SNLI, MultiNLI, and Adversarial NLI
Lecture 34 - Natural Language Inference: Adversarial Testing
Lecture 35 - Natural Language Inference: Modeling Strategies
Lecture 36 - Natural Language Inference: Attention
Lecture 37 - NLU and Information Retrieval: Overview
Lecture 38 - NLU and Information Retrieval: Classical IR
Lecture 39 - NLU and Information Retrieval: Neural IR, Part 1
Lecture 40 - NLU and Information Retrieval: Neural IR, Part 2
Lecture 41 - NLU and Information Retrieval: Neural IR, Part 3
Lecture 42 - Relation Extraction: Overview
Lecture 43 - Relation Extraction: Data Resources
Lecture 44 - Relation Extraction: Problem Formulation
Lecture 45 - Relation Extraction: Evaluation
Lecture 46 - Relation Extraction: Simple Baselines
Lecture 47 - Relation Extraction: Directions to Explore
Lecture 48 - Overview of Analysis Methods in NLP
Lecture 49 - Analysis Methods in NLP: Adversarial Testing
Lecture 50 - Analysis Methods in NLP: Adversarial Training (and Testing)
Lecture 51 - Analysis Methods in NLP: Probing
Lecture 52 - Analysis Methods in NLP: Feature Attribution
Lecture 53 - Overview of Methods and Metrics
Lecture 54 - Methods and Metrics: Classifier Metrics
Lecture 55 - Methods and Metrics: Natural Language Generation Metrics
Lecture 56 - Methods and Metrics: Data Organization
Lecture 57 - Methods and Metrics: Model Evaluation
Lecture 58 - Presenting Your Work: Final Papers
Lecture 59 - Writing NLP Papers
Lecture 60 - NLP Conference Submissions
Lecture 61 - Giving Talks
Lecture 62 - Conclusions