Transition-based dependency parsing with stack long short-term memory

Dyer, Chris; Ballesteros, Miguel; Ling, W; Matthews, A; Smith, Noah A.

Transition-based dependency parsing with stack long short-term memory

License

Citation

Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA. Transition-based dependency parsing with stack long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Volume 1: Long Papers; 2015 July 26-31; Beijing (China). [place unknown]: ACL; 2015. p. 334-43.

Abstract

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 the conventional stack data structures used in transitionbased parsing, elements can be pushed to/nor popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. This lets us formulate an efficient parsing model that captures three facets of a parser’s state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal/nstructures. Standard backpropagation techniques are used for training and yield state-of-the-art parsing performance.