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CMU Neural Nets for NLP 2017 - Advanced Search Algorithms
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- 1 Intro
- 2 Potential Problems
- 3 Dealing with disparity in actions Effective Inference for Generative Neural Parsing (Mitchell Stern et al., 2017)
- 4 Threshold based pruning 'Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation' (Y Wu et al. 2016)
- 5 More complicated normalization Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation' (Y Wu et al. 2016)
- 6 Beam Search-Benefits and Drawbacks
- 7 More beam search in training A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models (Goyal et al., 2017)
- 8 Classical A* parsing al., 2003
- 9 Is the heuristic admissible? Global Neural CCG Parsing with Optimality Guarantees (Lee et al. 2016)
- 10 Estimating future costs Learning to Decode for Future Success (Li et al., 2017)
- 11 Monte-Carlo Tree Search Human-like Natural Language Generation Using
