Span-Based Constituency Parser

This is an implementation of the span-based natural language constituency parser described in the paper Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles, which will appear in EMNLP (2016).

Required Dependencies

• Python 2.7
• NumPy
• DyNet (To ensure compatibility, check out and compile commit 71fc893eda8e3f3fccc77b9a4ae942dce77ba368)

Vocabulary Files

Vocabulary may be loaded every time from a training tree file, or it may be stored (separately from a trained model) in a JSON file, which is much faster and recommended. To learn the vocabulary from a file with training trees and write a JSON file, use a command such as the following:

python src/main.py --train data/02-21.10way.clean --write-vocab data/vocab.json

Training

Training requires a file containing training trees (--train) and a file containg validation trees (--dev), which are parsed four times per training epoch to determine which model to keep. A file name must also be provided to store the saved model (--model). The following is an example of a command to train a model with all of the default settings:

python src/main.py --train data/02-21.10way.clean --dev data/22.auto.clean --vocab data/vocab.json --model data/my_model

The following table provides an overview of additional training options:

Argument	Description	Default
--dynet-mem	Memory (MB) to allocate for DyNet	2000
--dynet-l2	L2 regularization factor	0
--dynet-seed	Seed for random parameter initialization	random
--word-dims	Word embedding dimensions	50
--tag-dims	POS embedding dimensions	20
--lstm-units	LSTM units (per direction, for each of 2 layers)	200
--hidden-units	Units for ReLU FC layer (each of 2 action types)	200
--epochs	Number of training epochs	10
--batch-size	Number of sentences per training update	10
--droprate	Dropout probability	0.5
--unk-param	Parameter z for random UNKing	0.8375
--alpha	Softmax weight for exploration	1.0
--beta	Oracle action override probability	0.0
--np-seed	Seed for shuffling and softmax sampling	random

Test Evaluation

There is also a facility to directly evaluate a model agaist a reference corpus, by supplying the --test argument:

python src/main.py --test data/23.auto.clean --vocab data/vocab.json --model data/my_model

Citation

If you use this software for research, we would appreciate a citation to our paper:

@inproceedings{cross2016span,
  title={Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles},
  author={Cross, James and Huang, Liang},
  journal={Empirical Methods in Natural Language Processing (EMNLP)},
  year={2016}
}