Abstract
Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. On the other hand, during incremental parsing, constituent information on the right hand side of the current word is not utilized, which is a relative weakness of shift-reduce parsing. To address this limitation, we leverage a fast neural model to extract lookahead features. In particular, we build a bidirectional LSTM model, which leverages full sentence information to predict the hierarchy of constituents that each word starts and ends. The results are then passed to a strong transition-based constituent parser as lookahead features. The resulting parser gives 1.3% absolute improvement in WSJ and 2.3% in CTB compared to the baseline, giving the highest reported accuracies for fully-supervised parsing.
Highlights
Transition-based constituent parsers are fast and accurate, performing incremental parsing using a sequence of state transitions in linear time
The third column shows the development parsing accuracies when the labels are used for lookahead features
A 3-layer Long Short-Term Memory (LSTM) does not give significant improvements compared to a 2-layer LSTM
Summary
Transition-based constituent parsers are fast and accurate, performing incremental parsing using a sequence of state transitions in linear time. Zhu et al (2013) exploit rich features by extracting history information from a parser stack, which consists of a sequence of non-local constituents. Due to the incremental nature of shiftreduce parsing, the right-hand side constituents of the current word cannot be used to guide the action at each step. Such lookahead features (Tsuruoka et al, 2011) correspond to the outside scores in chart parsing (Goodman, 1998), which has been effective for obtaining improved accuracies
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