I am interested in neural network models and learning algorithms which are able to discover, capture, represent, generalize, and transfer (meta)understanding for and across tasks of natural language understanding.

As the shift from natural language text processing to natural language understanding swiftly taking place, quite a few controversial issues have also been raised. Is structured prediction still important for neural network models? Is structure/linguistics necessary or a necessary evil? Are insights from neuroscience more relevant? Is language just a side effect of general intelligence?

These are all unsettled issues, but I'm open to test any idea.

Here is an outdated rant which contains a small sample of my interests.


Structured Learning with Inexact Search: Advances in Shift-Reduce CCG Parsing
Thesis don't-click-here pdf slides

LSTM Shift-Reduce CCG Parsing
Wenduan Xu
In EMNLP 2016 pdf code

Expected F-measure Training for Shift-Reduce Parsing with Recurrent Neural Networks
Wenduan Xu, Michael Auli, and Stephen Clark
In NAACL 2016 pdf slides code

Don’t Interrupt Me While I Type: Inferring Text Entered Through Gesture Typing on Android Keyboards
Laurent Simon, Wenduan Xu, and Ross Anderson
In PETS 2016 pdf blog
Andreas Pfitzmann Best Student Paper

CCG Supertagging with a Recurrent Neural Network
Wenduan Xu, Michael Auli, and Stephen Clark
In ACL 2015 (short paper) pdf slides code

Shift-Reduce CCG Parsing with a Dependency Model
Wenduan Xu, Stephen Clark, and Yue Zhang
In ACL 2014 pdf errata slides code talk

Learning to Prune: Context-Sensitive Pruning for Syntactic MT
Wenduan Xu, Yue Zhang, Philip Williams, and Philipp Koehn
In ACL 2013 (short paper) pdf poster code

Extending Hiero Decoding in Moses with Cube Growing
Wenduan Xu and Philipp Koehn
PBML pdf
(Presented at the 7th MT Marathon 2012.)