We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory ...
We introduce recurrent neural network grammars,/nprobabilistic models of sentences with/nexplicit phrase structure. We explain efficient/ninference procedures that allow application to/nboth parsing and language modeling. ...
We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like ...
