3 citations · 3 across the 1 of their papers we have counts for
5 papers
Syntactic Structure Distillation Pretraining For Bidirectional Encoders
Adhiguna Kuncoro, Lingpeng Kong, Daniel Fried +4
Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests…
Scalable Syntax-Aware Language Models Using Knowledge Distillation
Adhiguna Kuncoro, Chris Dyer, Laura Rimell +2
Prior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential langua…
Unsupervised Recurrent Neural Network Grammars
Yoon Kim, Alexander M. Rush, Lei Yu +3
Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence i…
Finding Syntax in Human Encephalography with Beam Search
John Hale, Chris Dyer, Adhiguna Kuncoro +1
Recurrent neural network grammars (RNNGs) are generative models of (tree,string) pairs that rely on neural networks to evaluate derivational choices. Parsing with them using beam s…
Dependency Parsing with LSTMs: An Empirical Evaluation
Adhiguna Kuncoro, Yuichiro Sawai, Kevin Duh +1
We propose a transition-based dependency parser using Recurrent Neural Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward neural network parser of Chen…