Completed
Linearization for Graph Structures (Konstas et al. 2017)
Class Central Classrooms beta
YouTube videos curated by Class Central.
Classroom Contents
Neural Nets for NLP 2018 - Neural Semantic Parsing
Automatically move to the next video in the Classroom when playback concludes
- 1 Tree Structures of Syntax
- 2 Representations of Semantics
- 3 Meaning Representations
- 4 Example Special-purpose Representations
- 5 Example Query Tasks
- 6 Example Command and Control Tasks
- 7 Example Code Generation Tasks
- 8 A Better Attempt: Tree-based Parsing Models • Generate from top-down using hierarchical sequence- to-sequence model (Dong and Lapata 2016)
- 9 Code Generation: Handling Syntax • Code also has syntax, e.g. in form of Abstract Syntax Trees
- 10 Problem w/ Weakly Supervised Learning: Spurious Logical Forms . Sometimes you can get the right answer without actually doing the generalizable thing (Guu et al. 2017)
- 11 Meaning Representation Desiderata (Jurafsky and Martin 17.1)
- 12 First-order Logic
- 13 Abstract Meaning Representation (Banarescu et al. 2013)
- 14 Other Formalisms
- 15 Parsing to Graph Structures
- 16 Linearization for Graph Structures (Konstas et al. 2017)
- 17 CCG and CCG Parsing
- 18 Neural Module Networks: Soft Syntax-driven Semantics (Andreas et al. 2016) . Standard syntax semantic interfaces use symbolic representations . It is also possible to use syntax to guide structure of…
- 19 Neural Models for Semantic Role Labeling . Simple model w/ deep highway LSTM tagger works well (Le et al. 2017)
