Source code for our EMNLP2021 paper: "Neuraling Regular Expressions for Slot Filling".

As the title we turn RE for slot filling into a trainable neural network.

cd src_seq && pip install -r requirements.txt

Please download the data files (data.zip) first using one of these links google drive, tencent drive. Then extract it under the /data folder.

unzip data.zip

Your directory structure should be like:

/RE2NN-SEQ
    /data
        /ATIS-BIO
        /ATIS-ZH-BIO
        /SNIPS-BIO
    /src_seq
    /model_seq
    ...

We provide the

• raw dataset files
• preprocessed dataset files
• embedding files (e.g. glove.300.emb)
• rule files (e.g. bio.rules.config)
• created and decomposed transducer files (e.g. /ATIS-BIO/automata/xxx.pkl)

Make sure you download the data files. model_seq contains example configs, you can directly run the code using the hyper-parameters in the configs and get the results using

python main.py --args_path ../model_seq/config_file_path.res

key parameters

• dataset (ATIS/ATIS-ZH/SNIPS)
• automata_path
• farnn (0/2)
• use_crf (0/1)
• method (decompose/onehot)
• use_bert (0/1)
• bert_finetune (0/1)

To preprocess your dataset and create vocab files, and pretrained static embeddings, make sure this step is correct! you need to modify function create_slot_dataset in data.py, and run it.

Our rules for slot filling is regular expressions with capturing groups. You can refer to the provided rule files data/xxx-BIO/rules.bio.config, they will help yo write your own rules.

The basic syntax is Sub-expression<:>Label, we aims to tag the content matched by sub-expressions as the provided label

The syntax symbol is a little bit different, $ is wildcard word, OO is wildcard label. We also support some handy shortcuts, for example, we allow defining a sub-expression variable, and support comments For example:

// class_type
@class_type@=(first class | coach class | coach | thrift)
$<:>OO * @class_type<:>class_type@ $<:>OO *

This RE will tag first class, coach class as B-class_type I-class_type and coach, thrift as B-class_type, no matter what former and latter contexts are.

You can check your rule's sanity by

cd src_seq/rule_utils
python rule_pre_parser.py --rule_path ../../data/ATIS-BIO/bio.rules.config

and check the parsed rule files, if some rules are missed, they may have syntax problems.

You can check the rule performance by first create transducer: (transducer can be viewed as a automata whose input vocabulary is the catesian product of the transducer's input vocabulary and output vocabulary)

cd src_seq/wfa
python create_dataset_automata.py --independent 2 --decompose 0 \
--rule_name bio.rules.config --dataset SNIPS-BIO --k_best 1 \
--automata_name my_rule

• FST: --independent 0
• i-FST: --independent 2

You will get automata files, my_rule.ID2 in data/SNIPS-BIO/automata, and a graph drawing the transducer

and then run the automata/rules

cd src_seq
python main.py --dataset SNIPS-BIO --method onehot \
--automata_path ../data/SNIPS-BIO/automata/my_rule.ID2 \
--normalize_automata none --rand_constant 0

To decompose the transducer

cd src_seq/wfa
python create_dataset_automata.py --independent 2 --decompose 1 \
--rule_name bio.rules.config --dataset SNIPS-BIO --k_best 3 \
--automata_name my_rule_decomposed

Run and train the decomposed transducer

cd src_seq/
python main.py --independent 2 --dataset SNIPS-BIO --method decompose \
--automata_path your_decomposed_automata.pkl --train_portion 1 --beta 0.1 \
--update_nonlinear tanh --lr 0.001