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Adoption with neural networks: CCG Parsing
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Classroom Contents
Neural Nets for NLP 2019 - Advanced Search Algorithms
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- 1 Intro
- 2 Why search?
- 3 Basic Pruning Methods (Steinbiss et al. 1994)
- 4 Prediction-based Pruning Methods (e.g. Stern et al. 2017)
- 5 Backtracking-based Pruning Methods
- 6 What beam size should use?
- 7 Variable length output sequences . In many tasks (eg MT), the output sequences will be of variable length
- 8 More complicated normalization Google's Neural Machine Translation System Bridging the Gap
- 9 Predict the output length (Eriguchi et al. 2016)
- 10 Why do Bigger Beams Hurt, pt. 2
- 11 Dealing with disparity in actions Ellective Inference for Generative Neural Parsing Mitchell Stam et al., 2017
- 12 Solution
- 13 Improving Diversity in top N Choices
- 14 Improving Diversity through Sampling
- 15 Sampling without Replacement (con't)
- 16 Monte-Carlo Tree Search Human-like Natural Language Generation Using Monte Carlo Tree Search
- 17 More beam search in training A Continuous Relaxation of Bear Search for End-to-end Training of Neural Sequence Models (Goyal et al., 2017)
- 18 Adoption with neural networks: CCG Parsing
- 19 Is the heuristic admissible? (Lee et al. 2016)
- 20 Estimating future costs Li et al., 2017
- 21 Actor Critic (Bahdanau et. al., 2017)
- 22 Actor Critic (continued)
- 23 A* search: benefits and drawbacks
- 24 Particle Filters (Buys et al., 2015)
- 25 Reranking (Dyer et al. 2016)
