Google Research

Recursive LSTM Tree Representation for Arc-Standard Transition-BasedDependency Parsing

Proceedings of the Third Workshop on Universal Dependencies (UDW, SyntaxFest 2019) (2019)


We propose a method to represent dependency trees as dense vectors through the re-cursive 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 positional representation used by \newcite{crossH16} and \newcite{kiperwasser2016simple}. 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.

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