This repository contains code for running CLINC and SNIPS experiments in the paper "Few-shot Slot Filling and Intent Classification with Retrieved Examples" (to appear in NAACL 2021).
To run the code, you need to first make sure your python version is below 3.8 because the code uses Tensorflow 1.x which is not supported in python 3.8. To check your python version, run
python --versionIf you have a python vesion >= 3.8, you may want to create a new enviroment with
a lower version of python, e.g. using conda.
Next install all the required packages and check out this repo.
cd /my/path/
pip install absl-py tensorflow==1.15 bert-tensorflow
pip install --no-deps -e \
git://github.com/google-research/language.git#egg=language
git clone https://github.com/google/retriever_parsingChoose and download a BERT model from
here and unzip models files at
/my/path/bert.
First, download CLINC data.
cd /my/path/
git clone https://github.com/clinc/oos-eval.gitThe data will be at /my/path/oos-eval/data/data_full.json.
cd /my/path/retriever_parsing
python clinc_similarity_train.py \
--data_dir=/my/path/oos-eval/data \
--data_output_dir=/my/path/oos-eval/preprocessed \
--bert_config_file=/my/path/bert/bert_config.json \
--vocab_file=/my/path/bert/vocab.txt \
--init_checkpoint=/my/path/bert/bert_model.ckpt \
--use_tpu=falseFirst, download SNIPS data from
here and unpack it in
/my/path/snips_data/. Assume the data is in /my/path/snips_data/ACL2020data.
Then preprocess data for each domain, e.g. AddToPlaylist.
python snips_preprocess_data.py \
--input_dir=/my/path/snips_data/ACL2020data \
--output_dir=/my/path/snips_data/ACL2020data/preprocessed \
--target_domain=AddToPlaylist \
--few_shot=5 \
--vocab_file=/my/path/bert/vocab.txtTo start training
python snips_similarity_train.py \
--data_dir=/my/path/snips_data/ACL2020data/preprocessed/5 \
--few_shot=5 \
--bert_config_file=/my/path/bert/bert_config.json \
--vocab_file=/my/path/bert/vocab.txt \
--init_checkpoint=/my/path/bert/bert_model.ckpt \
--target_domain=AddToPlaylist \
--use_tpu=false
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