Recursive LSTM Tree Representation for Arc-Standard Transition-Based Dependency Parsing

We propose a method to represent dependency trees as dense vectors through the recursive application of Long Short-Term Memory networks to build Recursive LSTM Trees (RLTs). We show that the dense vectors produced by Recursive LSTM Trees replace the need for structural features by using them as feature vectors for a greedy Arc-Standard transition-based dependency parser. We also show that RLTs have the ability to incorporate useful information from the bi-LSTM contextualized representation used by Cross and Huang (2016) and Kiperwasser and Goldberg (2016b). The resulting dense vectors are able to express both structural information relating to the dependency tree, as well as sequential information relating to the position in the sentence. The resulting parser only requires the vector representations of the top two items on the parser stack, which is, to the best of our knowledge, the smallest feature set ever published for Arc-Standard parsers to date, while still managing to achieve competitive results.