526 citations · 1.1k across the 32 of their papers we have counts for
4 papers · 2 filters
Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem, Abhishek Shah, Hui Wan +3
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…
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
Ethan Wilcox, Peng Qian, Richard Futrell +2
State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical…
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
Richard Futrell, Ethan Wilcox, Takashi Morita +3
We deploy the methods of controlled psycholinguistic experimentation to shed light on the extent to which the behavior of neural network language models reflects incremental repres…
Recursive Subtree Composition in LSTM-Based Dependency Parsing
Miryam de Lhoneux, Miguel Ballesteros, Joakim Nivre
The need for tree structure modelling on top of sequence modelling is an open issue in neural dependency parsing. We investigate the impact of adding a tree layer on top of a seque…