RE2RNN

Source code for the EMNLP2020 paper: "Cold-start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks", Chengyue Jiang, Yinggong Zhao, Shanbo Chu, Libin Shen, and Kewei Tu.

Citation

@inproceedings{jiang-etal-2020-cold,
    title = "Cold-start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks",
    author = "Jiang, Chengyue  and
      Zhao, Yinggong  and
      Chu, Shanbo  and
      Shen, Libin  and
      Tu, Kewei",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.258",
    pages = "3193--3207",
}

Requirements

• pytorch 1.3.1
• tensorly 0.5.0
• numpy
• tqdm
• automata-tools
• pyparsing

Data

Raw dataset files, preprocessed dataset files, glove word embedding matrix, rules for each dataset, and the decomposed automata files can be downloaded here: Google Drive, Tencent Drive.

You can download and extract the zip file, and replace the original data directory. The directory structure should be:

.
├── data
│   ├── ATIS
│   │   ├── automata
│   │   ├── ....
│   ├── TREC
│   │   ├── automata
│   │   ├── ....
│   ├── ....
├── src
│   ├── ....
├── src_simple
│   ├── ....
├── model
│   ├── ....
├── imgs
│   ├── ....

If you have done these, you can skip to the training part.

Regular Expressions

We provide the RE rules for three datasets, ATIS, QC(TREC-6) and SMS. Our REs are word-level, not char-level. We show the symbols and their meanings in the following table.

Symbol	Meaning
$	wildcard
%	numbers, e.g. 5, 1996
&	punctuations
?	0 or 1 occurrence
*	zero or more occurrences
+	one or more occurrences
(a|b)	a or b

Regular expressions Examples

ATIS - abbreviation label.

[abbreviation]
( $ * ( mean | ( stand | stands ) for | code ) $ * ) | ( $ * what is $ EOS )

SMS - spam label.

[spam]
$ * dating & * $ * call $*

Regular Expression to FA

We show examples on ATIS dataset, for other datasets, simply change --dataset option to TREC or SMS.

prepare the dataset

You need first download the GloVe 6B embeddings, and place the embedding files into data/emb/glove.6B/ You can also prepare the dataset from the raw dataset by running the following command.

python data.py --dataset ATIS

RE to FA

We turn the regular expressions into a finite automaton using our automata-tools package implemented by (@linonetwo). This tool is modified based on https://github.com/sdht0/automata-from-regex. This package require the 'dot' command for drawing the automata.

Or running the following command to convert REs/reversed RE (for backward direction) to FA.

python create_automata.py --dataset ATIS --automata_name all --reversed 0
python create_automata.py --dataset ATIS --automata_name all --reversed 1

The regular expression for ATIS - abbreviation mentioned above can be represented using following automaton.

Run the REs

The RE system's result is got by running the un-decomposed automaton you just created.

python main.py --model_type Onehot --dataset ATIS --only_probe 1 --wfa_type viterbi \
--epoch 0 --automata_path_forward all.1105155534-1604591734.6171093.split.pkl --seed 0

Decomposing FAs

We convert the FAs using tensor-rank decomposition.

Run the following command to convert REs to FA.

python decompose_automata.py --dataset ATIS --automata_name automata_name --rank 150 --init svd

FAs to FA-RNN and training FA-RNN.

To train the initialize the FA-RNNs on ATIS, SMS, and TREC make sure you finish the above steps. Then let's train an FA-RNN initialized by the decomposed automata. If you have downloaded automata and place them into the right location, you can run.

python main.py --dataset ATIS --run save_dir --model_type FSARNN --beta 0.9 \
 --wfa_type forward --seed 0 --lr 0.001 --bidirection 0 --farnn 0 --random 0

Please check the function get_automata_from_seed in utils/utils.py to understand which automaton you are using.

If you use newly decomposed automata, you need to specify the --automata_path_forward and --automata_path_backward options.

For example: FARNN

python main.py --dataset ATIS --run save_dir --model_type FSARNN --bidirection 0 \
--beta 0.9 --wfa_type forward --seed 0 --lr 0.001 --farnn 0 --normalize_automata l2 \
--automata_path_forward automata.newrule.split.randomseed150.False.0.0003.0.pkl

For example: BiFARNN

python main.py --dataset ATIS --run save_dir --model_type FSARNN --bidirection 1 \
--beta 0.9 --wfa_type forward --seed 0 --lr 0.001 --farnn 0 --normalize_automata l2 \
--automata_path_forward automata.newrule.split.randomseed150.False.0.0003.0.pkl \
--automata_path_backward automata.newrule.reversed.randomseed150.False.0.0735.0.pkl

For example: FAGRU

python main.py --dataset ATIS --run save_dir --model_type FSARNN --bidirection 0 \
--beta 0.9 --wfa_type forward --seed 0 --lr 0.001 --farnn 1 --normalize_automata l2 \
--automata_path_forward automata.newrule.split.randomseed150.False.0.0003.0.pkl

We also remove some options and unimportant code to provide a cleaner version of code in /src_simple, in which only contains FARNN related code. As an example:

python main_min.py --dataset ATIS --run save_dir --model_type FSARNN --beta 0.3

Interpretability and Models.

You first need to download the FA-RNN models and config files here: Google Drive, Tencent Drive. Please place the files in the /model directory.

To seed the log and hyper-parameters of these provided model, simple using pickle to load the .res config files. For example, to achieve the hyper-params for model D0.9739-T0.9653-DI0.8655-TI0.8645-1106095843-1604656723.555744-ATIS-0.model, you can run:

import pickle
pickle.load(open('1106095843-1604656723.5809364.res', 'rb'))

Note that some useless hyper-parameters in the config files are cleaned in the final version/simple version, so the config file may not be directly used, just filter out the useless hyper-params.

We provide several examples showing how to convert the trained model parameters back into WFAs, and threshold them into NFA. See the file jupyter/checkTrainedRules.ipynb.