Papers › Tree-Stack LSTM in Transition Based Dependency Parsing

Tree-Stack LSTM in Transition Based Dependency Parsing

1 Oct 2018CONLL 2018 10archive 2025-07-28

{\"O}mer K{\i}rnap, Erenay Dayan{\i}k, Deniz Yuret

We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks. Tree-stack LSTM does not use any parse tree based or hand-crafted features, yet performs better than models with these features. We also develop new set of embeddings from raw features to enhance the performance. There are 4 main components of this model: stack{'}s σ-LSTM, buffer{'}s β-LSTM, actions{'} LSTM and tree-RNN. All LSTMs use continuous dense feature vectors (embeddings) as an input. Tree-RNN updates these embeddings based on transitions. We show that our model improves performance with low resource languages compared with its predecessors. We participate in CoNLL 2018 UD Shared Task as the {``}KParse{''} team and ranked 16th in LAS, 15th in BLAS and BLEX metrics, of 27 participants parsing 82 test sets from 57 languages.

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Tasks

Dependency Parsing Lemmatization Morphological Analysis Morphological Tagging Transition-Based Dependency Parsing Word Embeddings

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Methods

LSTM Sigmoid Activation Tanh Activation

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