Salim Roukos

Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning

2019 · arXiv (Cornell University)

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments …

Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency

2023 · arXiv (Cornell University)

We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-supervised method for training a neural semantic parser. Conceptually, LOCCO can be viewed as a form of self-learning where the semantic parser being trained is …