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CMU Advanced NLP 2022 (21): Modeling Long Sequences
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Classroom Contents
CMU Advanced Natural Language Processing 2022
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- 1 CMU Advanced NLP 2022 (1): Introduction to NLP
- 2 CMU Advanced NLP 2022 (2): Text Classification
- 3 CMU Advanced NLP 2022 (3): Language Modeling and NN Basics
- 4 CMU Advanced NLP 2022 (4): Sequence Modeling and Recurrent Networks
- 5 CMU Advanced NLP 2022 (5): Conditioned Generation
- 6 CMU Advanced NLP 2022 (6): Attention
- 7 CMU Advanced NLP 2022 (7): Pre-training Methods
- 8 CMU Advanced NLP 2022 (8): Multi-task, Multi-domain, and Multi-lingual Learning
- 9 CMU Advanced NLP 2022 (9): Prompting
- 10 CMU Advanced NLP 2022 (10): How to use pre-trained models?
- 11 CMU Advanced NLP 2022 (11): Experimental Design
- 12 CMU Advanced NLP 2022 (12): Interpreting and Debugging NLP Models
- 13 CMU Advanced NLP 2022 (13): Text-based QA
- 14 CMU Advanced NLP 2022 (14): Bias and Fairness
- 15 CMU Advanced NLP 2022 (15): Dialog
- 16 CMU Advanced NLP 2022 (16): Information Extraction and Knowledge-based QA
- 17 CMU Advanced NLP 2022 (17): Word Segmentation and Morphology
- 18 CMU Advanced NLP 2022 (18): Syntax 1
- 19 CMU Advanced NLP 2022 (19): Syntax 2 and Semantics 1
- 20 CMU Advanced NLP 2022 (20): Semantics 2 and Discourse
- 21 CMU Advanced NLP 2022 (21): Modeling Long Sequences
- 22 CMU Advanced NLP 2022 (22): Structured Learning Algorithms
- 23 CMU Advanced NLP 2022 (23): Latent Variable Models
- 24 CMU Advanced NLP 2022 (24): Adversarial Methods for Text
