Medical Question Understanding

1. Papers included in this repository

"A Gradually Soft Multi-Task and Data-Augmented Approach to Medical Question Understanding"
Khalil Mrini, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas, Ndapa Nakashole
ACL 2021 (Main, Long Paper)
PDF | ACL Anthology | BibTeX | Video Presentation

"Joint Summarization-Entailment Optimization for Consumer Health Question Understanding"
Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, Ndapa Nakashole
NAACL 2021 workshop on NLP for Medical Conversations (NLPMC)
Best Student Paper Award
PDF | ACL Anthology | BibTeX

"UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization"
Khalil Mrini, Franck Dernoncourt, Seunghyun Yoon, Trung Bui, Walter Chang, Emilia Farcas, Ndapa Nakashole
NAACL 2021 workshop on Biomedical NLP (BioNLP)
PDF | ACL Anthology | BibTeX

2. Installation

Use the following commands to install this package:

pip install --editable ./
pip install transformers

Download pre-trained models like this:

mkdir models
cd models
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.xsum.tar.gz
tar -xzvf bart.large.xsum.tar.gz
rm bart.large.xsum.tar.gz
cd ..

3. Data preprocessing

Preprocess the MeQSum dataset as follows, where "MeQSum" is the same folder as the one (parent folder) where this repository is located:

sh ./examples/joint_rqe_sum/preprocess_MeQSum.sh .. MeQSum
mv MeQSum-bin ../MeQSum-bin

TASK=../MeQSum
for SPLIT in train dev
do
  for LANG in source target
  do
    python -m examples.roberta.multiprocessing_bpe_encoder \
    --encoder-json encoder.json \
    --vocab-bpe vocab.bpe \
    --inputs "$TASK/$SPLIT.$LANG" \
    --outputs "$TASK/$SPLIT.bpe.$LANG" \
    --workers 60 \
    --keep-empty;
  done
done

fairseq-preprocess \
  --source-lang "source" \
  --target-lang "target" \
  --trainpref "${TASK}/train.bpe" \
  --validpref "${TASK}/dev.bpe" \
  --destdir "${TASK}-bin/" \
  --workers 60 \
  --srcdict dict.txt \
  --tgtdict dict.txt;

4. Training Commands

To train the model with the Gradually Soft Parameter Sharing loss on MeQSum:

TOTAL_NUM_UPDATES=810 
WARMUP_UPDATES=81
LR=3e-05
MAX_TOKENS=512
UPDATE_FREQ=4
BART_PATH=./models/bart.large.xsum/model.pt

CUDA_VISIBLE_DEVICES=1 fairseq-train ../MeQSum-bin \
    --restore-file $BART_PATH \
    --max-tokens $MAX_TOKENS \
    --task joint_rqe_sum \
    --source-lang source --target-lang target \
    --truncate-source \
    --layernorm-embedding \
    --share-all-embeddings \
    --share-decoder-input-output-embed \
    --batch-size 8 \
    --reset-optimizer --reset-dataloader --reset-meters \
    --required-batch-size-multiple 1 \
    --arch bart_large_gradsoft \
    --criterion grad_soft --add-prev-output-tokens \
    --label-smoothing 0.1 \
    --dropout 0.1 --attention-dropout 0.1 \
    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \
    --clip-norm 0.1 \
    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
    --update-freq $UPDATE_FREQ \
    --skip-invalid-size-inputs-valid-test \
    --find-unused-parameters;

To train the model with the Joint Summarization-Entailment loss on MeQSum:

TOTAL_NUM_UPDATES=810 
WARMUP_UPDATES=81
LR=3e-05
MAX_TOKENS=512
UPDATE_FREQ=4
BART_PATH=./models/bart.large.xsum/model.pt

CUDA_VISIBLE_DEVICES=0 fairseq-train ../MeQSum-bin \
    --restore-file $BART_PATH \
    --max-tokens $MAX_TOKENS \
    --task joint_rqe_sum \
    --source-lang source --target-lang target \
    --truncate-source \
    --layernorm-embedding \
    --share-all-embeddings \
    --share-decoder-input-output-embed \
    --beam 2 \
    --batch-size 8 \
    --reset-optimizer --reset-dataloader --reset-meters \
    --required-batch-size-multiple 1 \
    --arch bart_large \
    --criterion joint_rqe_sum --add-prev-output-tokens \
    --label-smoothing 0.1 \
    --dropout 0.1 --attention-dropout 0.1 \
    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 --no-epoch-checkpoints --no-last-checkpoints \
    --clip-norm 0.1 \
    --max-epoch 10 \
    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
    --update-freq $UPDATE_FREQ \
    --skip-invalid-size-inputs-valid-test \
    --find-unused-parameters;

To train the model with the Summarization loss on MeQSum:

TOTAL_NUM_UPDATES=5400 
WARMUP_UPDATES=81
LR=3e-05
MAX_TOKENS=512
UPDATE_FREQ=4
BART_PATH=./models/bart.large.xsum/model.pt

CUDA_VISIBLE_DEVICES=1 fairseq-train ../MeQSum-bin \
    --restore-file $BART_PATH \
    --max-tokens $MAX_TOKENS \
    --task translation \
    --source-lang source --target-lang target \
    --truncate-source \
    --layernorm-embedding \
    --share-all-embeddings \
    --share-decoder-input-output-embed \
    --batch-size 8 \
    --reset-optimizer --reset-dataloader --reset-meters \
    --required-batch-size-multiple 1 \
    --arch bart_large \
    --criterion label_smoothed_cross_entropy \
    --label-smoothing 0.1 --max-epoch 100 \
    --dropout 0.1 --attention-dropout 0.1 \
    --weight-decay 0.01 --optimizer adam --adam-betas "(0.9, 0.999)" --adam-eps 1e-08 \
    --clip-norm 0.1 \
    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
    --update-freq $UPDATE_FREQ \
    --skip-invalid-size-inputs-valid-test --no-epoch-checkpoints --no-last-checkpoints \
    --find-unused-parameters;

Fairseq License

fairseq(-py) is MIT-licensed. The license applies to the pre-trained models as well.

Fairseq Citation

Please cite as:

@inproceedings{ott2019fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}