343 citations · 379 across the 6 of their papers we have counts for
6 papers
Transition-Based Dependency Parsing using Perceptron Learner
Rahul Radhakrishnan Iyer, Miguel Ballesteros, Chris Dyer +1
Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models th…
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…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…
What Do Recurrent Neural Network Grammars Learn About Syntax?
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong +3
Recurrent neural network grammars (RNNG) are a recently proposed probabilistic generative modeling family for natural language. They show state-of-the-art language modeling and par…