This is the reposity for reproducing "Unsupervised Domain Adaptation for Question Generation with Domain Data Selection and Self-training".

Data preprocess

• Natural Question

python nq_preprocess.py --data_file path_to/Google_Natural_Question/v1.0-simplified_simplified-nq-train.jsonl --outdir ../data/nq --prefix train 
python nq_preprocess.py --data_file path_to/Google_Natural_Question/v1.0-simplified_nq-dev-all.jsonl  --outdir ../data/nq  --prefix dev

• SQuAD

python squad_preprocess.py --infile ../data/squad/train-v1.1.json --outdir ../data/squad --prefix train
python squad_preprocess.py --infile ../data/squad/dev-v1.1.json --outdir ../data/squad --prefix dev 

• RACE: race_preprocess.py

• SciQ: sciq_preprocess.py

• MLQuestions: mlquestions_preprocess.py

Domain discriminator

cd preprocess/domain_discriminator

Unsupervised Domain Clustering

• Create BERT encoding for each domain, and perform clustering.

python domain_data_selec_with_UDC.py

• Visualization Analysis, and create selected data for each domain.

(jupyternotebook) interactive
data_selection_UDC_analysis.ipynb

Base model training

The base model and part of the code are adopted from unilm.

• NQ: ./run_fine_tune_nq_unilm.sh
• RACE: ./run_fine_tune_race_unilm.sh
• SciQ: ./run_fine_tune_sciq_unilm.sh

Transfer

with Random selected data.

./run_fine_tune_nq_random_selection.sh 1000

Re-fine-tuning NQ for RACE

• with gmm (l2 distance) RACE order: ./run_fine_tune_nq_by_race_gmm_l2_order.sh 1000

Re-fine-tuning NQ for SciQ

• with gmm (l2 distance) SciQ order: ./run_fine_tune_nq_by_sciq_gmm_l2_order.sh 1000

Fine-tune with Pseudo-Labeling

RACE

• pseudo-labeling only, no filter: ./run_uda_race_no_filter_pseudo-only.sh
• pseudo-labeling only, fluency: ./run_uda_race_fluency_pseudo-only.sh 10.5
• pseudo-labeling only, perplexity: run_uda_race_perplexity_pseudo-only.sh 8.5
• pseudo-labeling only, fluency && perplexity: ./run_uda_race_fluency_and_PPL_pseudo-only.sh 10.5 8.5
• Fluency: run_uda_race_fluency_reine-tuned.sh 10.5
• Perplexity: run_uda_race_perplexity_reine-tuned.sh 8.5
• Fleuncy + Perplexity: ./run_uda_race_fluency_and_PPL_reine-tuned.sh 10.5 8.5

Selected data + Pseudo-Labeling

• No Filter: ./run_uda_race_no_filter_ds+pl.sh
• Fluency: ./run_uda_race_fluency_ds+pl.sh 10.5
• Perplexity: ./run_uda_race_perplexity_ds+pl.sh 8.5
• Fluency + Perplexity: ./run_uda_race_fluency_and_PPL_ds+pl.sh 10.5 8.5

Decoding

NQ

extract src from dev set to nq_unilm_ckpt/src.txt, and lower case of tgt to nq_unilm_ckpt/gold.txt, for further evaluation.

run decoding.

./run_unilm_decoding.sh nq_unilm_ckpt/nq_random_ckpt/epoch-10/ ../../data/MLQuestions/test.jsonl 0,1

run evaluation

./score.sh squad_unilm_ckpt/ckpt/ squad_unilm_ckpt/gold.txt squad_unilm_ckpt/src.txt