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1
Intro
2
Documentlevel Language Modeling
3
Recurrent Neural Networks
4
Encoding Methods
5
Self Attendance
6
Transformer Excel
7
Compressive Transformer
8
Sparse Transformer
9
Adaptive Span Transformer
10
Sparse Computations
11
Reformer
12
Low Rank Approximation
13
Evaluation
14
Entity Coreference
15
Mention Detection
16
Components
17
Instances
18
Pair Models
19
Coreference
20
Coreference model
21
Coreference models
22
Discourse parsing
23
Neural models
Description:
Explore advanced natural language processing techniques for document-level modeling in this comprehensive lecture from CMU's Advanced NLP course. Delve into extracting features from long sequences, coreference resolution, and discourse parsing. Learn about various encoding methods, including self-attention and Transformer models, as well as efficient approaches like sparse and adaptive span transformers. Discover techniques for entity coreference, including mention detection and pair models. Examine neural models for discourse parsing and gain insights into evaluating document-level language models. Access additional resources and materials through the provided class website to further enhance your understanding of these advanced NLP concepts.

CMU Advanced NLP: Document-Level Modeling

Graham Neubig
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