Transition-based dependency parsing with heuristic backtracking

Buckman, Jacob; Ballesteros, Miguel; Dyer, Chris

Transition-based dependency parsing with heuristic backtracking

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Citation

Buckman J, Ballesteros M, Dyer C. Transition-based dependency parsing with heuristic backtracking. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing; 2016 Nov 1-5; Austin, Texas, USA. Stroudsburg (USA): Association for Computational Linguistics (ACL); 2016. p. 2313-18.

Abstract

We introduce a novel approach to the decoding problem in transition-based parsing: heuristic backtracking. This algorithm uses a series of partial parses on the sentence to locate the best candidate parse, using confidence estimates/nof transition decisions as a heuristic to guide the starting points of the search. This allows us to achieve a parse accuracy comparable to beam search, despite using fewer transitions. When used to augment a Stack-LSTM transition-based parser, the parser shows an unlabeled attachment score of up to 93.30% for English and 87.61% for Chinese.