Depth-bounded grammar Induction Model with Inside-sampling
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README.md
dimi-trainer.py
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README.md

Depth-bounded grammar Induction Model with Inside-sampling

This repo is for the EMNLP 2018 paper Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction. Please cite the paper if you use this repo in your work:

@inproceedings{Jin2018b,
author = {Jin, Lifeng and Doshi-Velez, Finale and Miller, Timothy A and Schuler, William and Schwartz, Lane},
booktitle = {EMNLP},
title = {{Depth-bounding is effective: Improvements and evaluation of unsupervised PCFG induction}},
year = {2018}
}

This repo is built partly on UHHMM and DB-PCFG repos.

Prerequisits

Required Python3 packages

  • bidict
  • numba
  • numpy
  • nltk
  • scipy
  • pyzmq

Usage

Single Machine

Using this repo is very straightforward.

  1. You need a file which is tokenized, delimited by space for words and one sentence per line.
  2. Then use utils/make_ints_file.py to process the file into two files, {*}.ints and {*}.dict.
  3. Create a config file following the format below. You can also find a sample config file in the repo.
  4. You can run python dimi-trainer.py config-file.ini now.
  5. Useful files will be generated in a output directory.
[io]
input_file = ./genmodel/ptb_20.dev.linetoks.ints
output_dir = ./outputs/WSJ20
dict_file = ./genmodel/ptb_20.dev.linetoks.dict

[params]
iters = 700
K = 15
init_alpha = .2
cpu_workers = 18
D = 2
batch_per_worker = 200

input_file: path to a ints file output_dir: path to a directory where the output files will be written dict_file: path to a dict file iters: total number of iterations to run K: total number of non-terminal categories init_alpha: the symmetric Dirichlet prior parameter cpu_workers: number of processes D: maximum allowed depthf for induced grammars batch_per_worker: number of sentences per batch per worker

Cluster

It is also easy to use this with a cluster. The steps are essentially the same as the single machine usage. The only differences are:

  1. You need to set cpu_workers to 0 in your config to tell the master that it is a master.
  2. You need to run python start_cluster_worker/py --config-path . for each worker you want to launch. This may be achieved in a cluster by submitting an array job and ask for one core for each worker.
  3. That's it.

Continue a run

You use a config file only when you want to start a new run. In order to continue an old run, you can do python dimi-trainer.py the/path/to/output_dir, and the program will read the models and config from there to continue a run. The single machine and cluster steps still apply.

Output

After running for a while, you may see some files in your output directory. First, the output directory is named like this : output_dir_D*K*A*_i, where *s are hyper-parameters set by you, and i is the index of the directory to avoid overwriting. Therefore you can run multiple experiments with the same config file without worrying about they will overwrite each other.

In your output directory, these useful files may be generated:

  • iter_{k}.linetrees.gz: k is the iteration number. These are the sampled trees for the corpus you use. You should be able to view them with zless or less directly.
  • pcfg_model_{k}.pkl: The program saves the whole PCFG model for the workers to use, but only keeps the most recent three model files around, as well as every 100th model. These are the models that are saved.
  • pcfg_hypparams.txt: This is a log file for some important runtime statistics. The commonly used ones include loglikelihood and right-branching score.
  • pcfg_counts_info.txt: Records the raw counts of each non-terminals categories. The total occurrences and the terminal only occurrences are recorded.
  • log.txt: This is a general log file of runtime information with timestamps.

Other helpful scripts

Please visit the utils folder to view its Readme for notes on some convenience tools this repo supplies.

Example

You can start an example run by doing python dimi-trainer.py config/config.ini with the repo. It runs the center-embedding synthetic dataset provided in datasets. You should see with a high probability that the run converges at around -1880 loglikelihood with a right-branching score of 0.2. There is a chance you are not able to get these results on the first try, so please try a couple more runs.