SEC

This is the implementation of the IEEE AIKE'2022 paper Few-shot Text Classification with Saliency-equivalent Concatenation (SEC).

Introduction to SEC

SEC is an unsupervised data augmentation approach for creating additional key information for a given sentence.

In our experiments (paper link), SEC improves few-shot text classification with several meta-learning models and can be applied to any text classification task.

Run our code of SEC

Install required packages

pip install -r requirements.txt
python -m spacy download en_core_web_sm

Prepare folders

mkdir experiments

Prepare raw data

Using T5

• File with one sentence per line.
  • An example file huffpost_test.txt is located in the SEC/data folder.

Choose the pre-trained T5 model

• English: t5-large
• Japanese (currently not stable):
  1. megagonlabs/t5-base-japanese-web
  2. sonoisa/t5-base-japanese
  3. google/mt5-large (multilingual T5)

Run the code

• Set gen_model_name with the desired pre-trained T5 model name
  • E.g., gen_model_name=google/mt5-large
• An example shell script run_sec.sh is located in the SEC/scripts folder.

Output tokens per line. (specify output_tokens)

gpu_id=0
gen_model_name=google/mt5-large
cls_model_name=roberta-large-mnli

python src/t5_summarize.py \
--gpu_id $gpu_id \
--data_path data/huffpost_test.txt \
--batch_size 4 \
--gen_model_name $gen_model_name \
--cls_model_name $cls_model_name \
--decoding_strategy 'top-k' \
--topk_value 40 \
--num_generate_per_sentence 10 \
--filter_mode $mode \
--output_length 128 \
--output_tokens

Or you can output text string per line. (without --output_tokens)

gpu_id=0
gen_model_name=google/mt5-large
cls_model_name=roberta-large-mnli

python src/t5_summarize.py \
--gpu_id $gpu_id \
--data_path data/huffpost_test.txt \
--batch_size 4 \
--gen_model_name $gen_model_name \
--cls_model_name $cls_model_name \
--decoding_strategy 'top-k' \
--topk_value 40 \
--num_generate_per_sentence 10 \
--filter_mode $mode \
--output_length 128

Output files

Example input filename: data/huffpost_test.txt

Using T5

1. Generated sentences (.txt)
   • Default: experiments/t5-large_huffpost_test_10N_topk_40_l128.txt
2. Concatenated sentences (.json)
   • Default: data/t5-large_huffpost_test_10N_topk_40_l128_roberta-large-mnli_E_only.json

Parameters

• gpu_id: ID for GPU usage
• data_path: file path of data (one sentence per line)
• batch_size: test batch size for model inference
• gen_model_name: generation model
• cls_model_name: model for sentence filtering
• decoding_strategy: decoding technique during sentence generation
• topk_value: value for top-k sampling (specify one of topk_value, topp_value, and beam_size)
• topp_value: value for top-p sampling (specify one of topk_value, topp_value, and beam_size)
• beam_size: value for beam search (specify one of topk_value, topp_value, and beam_size)
• num_generate_per_sentence: number of synthetic sentences outputed by the generation model
• filter_mode: mode for NLI (natural language inference) during sentence filtering
• output_length: the maximum length of T5-output sequences
• output_tokens: If specified, tokens will be saved in the output file (.json ).

Cite our paper

Please cite our paper if you use our code. Thank you!

@INPROCEEDINGS{9939269,
  author={Lin, Ying-Jia and Chang, Yu-Fang and Kao, Hung-Yu and Wang, Hsin-Yang and Liu, Mu},
  booktitle={2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)}, 
  title={Few-shot Text Classification with Saliency-equivalent Concatenation}, 
  year={2022},
  volume={},
  number={},
  pages={74-81},
  doi={10.1109/AIKE55402.2022.00019}}