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Apr. 6, 2017
Created
Apr. 6, 2016

Recurrent Neural Network Grammars

Code for the Recurrent Neural Network Grammars paper (NAACL 2016), by Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith, after the Corrigendum (last two pages on the ArXiv version of the paper). The code is written in C++.

Citation

            @inproceedings{dyer-rnng:16,
             author = {Chris Dyer and Adhiguna Kuncoro and Miguel Ballesteros and Noah A. Smith},
             title = {Recurrent Neural Network Grammars},
             booktitle = {Proc. of NAACL},
             year = {2016},
            } 

Prerequisites

  • A C++ compiler supporting the C++11 language standard
  • Boost libraries
  • Eigen (latest development release)
  • CMake
  • EVALB (latest version. IMPORTANT: please put the EVALB folder on the same directory as get_oracle.py and sample_input_chinese.txt to ensure compatibility)

Build instructions

Assuming the latest development version of Eigen is stored at: /opt/tools/eigen-dev

mkdir build
cd build
cmake -DEIGEN3_INCLUDE_DIR=/opt/tools/eigen-dev ..
make -j2

Sample input format:

sample_input_english.txt (English PTB) and sample_input_chinese.txt (Chinese CTB)

Oracles

The oracle converts the bracketed phrase-structure tree into a sequence of actions.
The script to obtain the oracle also converts singletons in the training set and unknown words in the dev and test set into a fine-grained set of 'UNK' symbols

Obtaining the oracle for the discriminative model

python get_oracle.py [training file] [training file] > train.oracle
python get_oracle.py [training file] [dev file] > dev.oracle
python get_oracle.py [training file] [test file] > test.oracle

Obtaining the oracle for the generative model

python get_oracle_gen.py [training file] [training file] > train_gen.oracle
python get_oracle_gen.py [training file] [dev file] > dev_gen.oracle
python get_oracle_gen.py [training file] [test file] > test_gen.oracle

Discriminative Model

The discriminative variant of the RNNG is used as a proposal distribution for decoding the generative model (although it can also be used for decoding on its own). To save time we recommend training both models in parallel.

On the English PTB dataset the discriminative model typically converges after about 3 days on a single-core CPU device.

Training the discriminative model

nohup build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -d [dev_oracle_file] -C [original_dev_file (PTB bracketed format, see sample_input_english.txt)] -P -t --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -D 0.2 > log.txt

IMPORTANT: please run the command at the same folder where remove_dev_unk.py is located.

If not using pre-trained word embedding, then remove the --pretrained_dim and -w flags.

The training log is printed to log.txt (including information on where the parameter file for the model is saved to, which is used for decoding under the -m option below)

Decoding with discriminative model

build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -p [test_oracle_file] -C [original_test_file (PTB bracketed format, see sample_input_english.txt)] -P --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -m [parameter file] > output.txt

Note: the output will be stored in /tmp/parse/parser_test_eval.xxxx.txt and the parser will output F1 score calculated with EVALB with COLLINS.prm option. The parameter file (following the -m in the command above) can be obtained from log.txt.

If training was done using pre-trained word embedding (by specifying the -w and --pretrained_dim options) or POS tags (-P option), then decoding must alo use the exact same options used for training.

Generative Model

The generative model achieved state of the art results, and decoding is done using sampled trees from the trained discriminative model
For the best results the generative model takes about 7 days to converge.

Training the generative model

nohup build/nt-parser/nt-parser-gen -x -T [training_oracle_generative] -d [dev_oracle_generative] -t --clusters clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -D 0.3 > log_gen.txt

The training log is printed to log_gen.txt, including information on where the parameters of the model is saved to, which is used for decoding later.

Decoding with the generative model

Decoding with the generative model requires sample trees from the trained discriminative model

Sampling trees from the discriminative model

 build/nt-parser/nt-parser --cnn-mem 1700 -x -T [training_oracle_file] -p [test_oracle_file] -C [original_test_file (PTB bracketed format, see sample_input_english.txt)] -P --pretrained_dim [dimension of pre-trained word embedding] -w [pre-trained word embedding] --lstm_input_dim 128 --hidden_dim 128 -m [parameter file of trained discriminative model] --alpha 0.8 -s 100 > test-samples.props 

important parameters

  • s = # of samples (all reported results used 100)
  • alpha = posterior scaling (since this is a proposal, a higher entropy distribution is probably good, so a value < 1 is sensible. All reported results used 0.8)

Prepare samples for likelihood evaluation

utils/cut-corpus.pl 3 test-samples.props > test-samples.trees

Evaluate joint likelihood under generative model

build/nt-parser/nt-parser-gen -x -T [training_oracle_generative] --clusters clusters-train-berk.txt --input_dim 256 --lstm_input_dim 256 --hidden_dim 256 -p test-samples.trees -m [parameters file from the trained generative model, see log_gen.txt] > test-samples.likelihoods

Estimate marginal likelihood (final step to get language modeling ppl)

utils/is-estimate-marginal-llh.pl 2416 100 test-samples.props test-samples.likelihoods > llh.txt 2> rescored.trees
  • 100 = # of samples
  • 2416 = # of sentences in test set
  • rescored.trees will contain the samples reranked by p(x,y)

The file llh.txt would contain the final language modeling perplexity after marginalization (see the last lines of the file)

Compute generative model parsing accuracy (final step to get parsing accuracy from the generative model)

utils/add-fake-preterms-for-eval.pl rescored.trees > rescored.preterm.trees
utils/replace-unks-in-trees.pl [Discriminative oracle for the test file] rescored.preterm.trees > hyp.trees    
utils/remove_dev_unk.py [gold trees on the test set (same format as sample_input_english.txt)] hyp.trees > hyp_final.trees
EVALB/evalb -p COLLINS.prm [gold trees on the test set (same format as sample_input_english.txt)] hyp_final.trees > parsing_result.txt

The file parsing_result.txt contains the final parsing accuracy using EVALB

Contact

If there are any issues, please let us know at adhiguna.kuncoro [ AT SYMBOL ] gmail.com, miguel.ballesteros [AT SYMBOL] ibm.com, and cdyer [AT SYMBOL] cs.cmu.edu

License

This software is released under the terms of the Apache License, Version 2.0