This repository contains the code used to train the parsers described in the paper Deep Biaffine Attention for Neural Dependency Parsing. Here we describe how the source code is structured and how to train/validate/test models.
lib/linalg.py: This file contains general-purpose functions that don't require any knowledge of hyperparameters. For example, the
bilinearfunctions, which simply return the result of applying an affine or biaffine transformation to the input.
configurable.py: This file contains the Configurable class, which wraps a
SafeConfigParserthat stores model hyperparameter options (such as dropout keep probability and recurrent size). Most or all classes in this repository inherit from it.
lib/models/nn.py: This file contains the
NNclass, which inherits from
Configurable. It contains functions such as
RNNthat are general-purpose but require knowledge of model hyperparameters.
lib/models/rnn.py: This file contains functions for building tensorflow recurrent neural networks. It is largely copied and pasted tensorflow source code with a few modifications to include a dynamic bidirectional recurrent neural network (rather than just a dynamic unidirectional one, which was all that was available when this project was started) and same-mask recurrent dropout.
lib/models/parsers: This directory contains different parser architectures. All parsers inherit from
BaseParser, which in turn inherits from
NN. The README in that directory details the differences between architectures.
lib/rnn_cells: This directory contains a number of different recurrent cells (including LSTMs and GRUs). All recurrent cells inherit from
BaseCellwhich inherits from
NN). The README in that directory details the different cell types.
lib/optimizers: This directory contains the optimizer used to optimize the network. All optimizers inherit from
BaseOptimizerwhich inherits from
NN). See the README in that directory for further explanation.
vocab.py: This file contains the
Vocabclass, which manages a vocabulary of discrete strings (tokens, POS tags, dependency labels).
bucket.py: This file contains the
Bucketclass, which manages all sequences of data up to a certain length, and pads everything shorter than that length with special tokens.
metabucket.py: This file contains the
Metabucketclass, which manages a group of multiple buckets, efficiently determining which bucket a new sentence goes in.
dataset.py: This file contains the
Datasetclass, which manages an entire dataset (e.g. the training set or the test set), reading in a conll file and grabbing minibatches.
network.py: This file contains the
Networkclass, which manages the training and testing of a neural network. It contains three
Datasetobjects--one for the training set, one for the validation set, and one for the test set--three
Vocabobjects--one for the words, one for the POS tags, and one for the dependency labels--one
NNobject--a parser architecture or other user-defined architecutre--and a
BaseOptimizerobject (stored in the
self._opsdictionary). This is also the file you call to run the network.
After downloading the repository, you will need a few more things:
We will assume that the dataset has been downloaded and exists in the directory
data/EWTand the word embeddings exist in
All configuration options can be specified on the command line, but it's much easier to instead store them in a configuration file. This includes the location of the data files. We recommend creating a new configuration file
config/myconfig.cfgin the config directory:
[OS] embed_dir = data/glove embed_file = %(embed_dir)s/en.100d.txt data_dir = data/EWT train_file = %(data_dir)s/train.conllu valid_file = %(data_dir)s/dev.conllu test_file = %(data_dir)s/test.conlluThis is also where other options can be specified; for example, to use the same configuration options used in the paper, one would also add ``` [Layers] n_recur = 4
[Dropout] mlpkeepprob = .67 ffkeepprob = .67
[Regularization] l2_reg = 0
[Radam] chi = 0
[Learning rate] learningrate = 2e-3 decaysteps = 2500 ```
The model can be trained with
bash python network.py --config_file config/myconfig.cfg --save_dir saves/mymodelThe
savesdirectory must already exist. It will attempt to create a
saves/mymodelalready exists, it will warn the user and ask if they want to continue. This is to prevent accidentally overwriting trained models. The model then reads in the training files and prints out the shapes of each bucket. By default, all matrices are initialized orthonormally; in order to generate orthonormal matrices, it starts with a random normal matrix and optimizes it to be orthonormal (on the CPU, using numpy). The final loss of this is printed, so that if the optimizer diverges (which is very rare but does occasionally happen) the researcher can restart.
Durint training, the model prints out training and validation loss, labeled attachment accuracy, and runtime (in sentences/second). During validation, the model also generates a
sanitycheck.txtfile in the save directory that prints out the model's predictions on sentences in the validation file. It also saves
history.pklto the save directory, which records the model's training and validation loss and accuracy. At this stage the model makes no attempt to ensure that the trees are well-formed and it makes no attempt to ignore punctuation.
The model will periodically save its tensorflow state so that it can be reloaded in the event of a crash or accidental termination. If the researcher wishes to terminate the model prematurely, they can do so with; in this event, they will be prompted to save the model with or discard it with another .
The model can be validated with
bash python network.py --save_dir saves/mymodel --validate python network.py --save_dir saves/mymodel --testThis creates a parsed copy of the validation and test files in the save directory. The model also reports unlabeled and labeled attachment accuracy in
saves/mymodel/scores.txt, but these calculate punctuation differently from what is standard. One should instead use the perl script in
binto compute accuracies:
bash perl bin/eval.pl -q -b -g data/EWT/dev.conllu \ -s saves/mymodel/dev.conllu \ -o saves/mymodel/dev.scores.txtStatistical significance between two models can similarly be computed using a perl script:
bash perl bin/compare.pl saves/mymodel/dev.scores.txt saves/defaults/dev.scores.txt
The current build is designed for research purposes, so explicit functionality for parsing texts is not currently supported.
config.cfg: A configuration file containing the model hyperparameters. Since hyperparameters can come from a variety of different sources (including multiple config files and command line arguments), this is necessary for restoring it later and remembering what hyperparameters were used.
HEAD: The github repository head--keeps track of the current github build, so that if the current github version is incompatible with the trained model, the researcher knows which commit they need to restore to run it.
history.pkl: A python pickle file containing a dictionary of training and validation history.
-trained-(.txt): tensorflow model after training for iterations.
rels.txt: Vocabulary files containing all words/tags/labels in the training set and their frequency, sorted by frequency.
sanitycheck.txt: The model's validation output. The sentences are grouped by bucket, not in the original order they were observed in the file, and the parses are chosen greedily rather than using any MST parsing algorithm to ensure well-formedness. Predicted heads/relations are put in second-to-last two columns, and gold heads/relations are put in the last two columns.
scores.txt: The model's self-reported unlabeled/labeled accuracy scores. As previously stated, don't trust these numbers too much--use the perl script.
test.conllu: The parsed validation and test datasets.