We explicitly separate diversification from generation using a mixture-of-experts content selection module (called Selector) that guides an encoder-decoder model.
Diverse Content Selection (one-to-many): Selector samples different binary masks (called focus; m1, m2, and m3 in the figure) on a source sequence.
Focused Generation (one-to-one): an encoder-decoder model generates different sequences from the source sequence guided by different masks.
Not only does this improve diversity of the generated sequences, but also improves accuracy (high fidelity) of them, since conventional models often learn suboptimal mapping that is in the middle of the targets but not near any of them.
pip install -r requirements.txt
or manually install the packages below.
torch==1.1
nltk
pandas
tqdm
pyyaml
git+git://github.com/bheinzerling/pyrouge
# From https://github.com/falcondai/pyrouge/tree/9cdbfbda8b8d96e7c2646ffd048743ddcf417ed9
wget https://www.dropbox.com/s/dl/zqhvtgfg40h3g3l/rouge_1.5.5.zip
unzip rouge_1.5.5.zip
mv RELEASE-1.5.5 utils/rouge
# Download preprocessed data at ./squad/, ./cnndm/ and ./glove/ respectively
wget https://www.dropbox.com/s/dl/0gtz5ckh3ie55oq/emnlp2019focus_redistribute.zip
unzip emnlp2019focus_redistribute.zip
# Generate train_df.pkl, val_df.pkl, test_df.pkl and vocab.pkl at ./squad_out/
python QG_data_loader.py
# Generate train_df.pkl, val_df.pkl, test_df.pkl and vocab.pkl at ./cnndm_out/
python CNNDM_data_loader.py
Details of dataset source are at Dataset_details.md
You can see more configurations in configs.py
1) Question Generation
python train.py --task=QG --model=NQG --load_glove=True --feature_rich --data=squad \
--rnn=GRU --dec_hidden_size=512 --dropout=0.5 \
--batch_size=64 --eval_batch_size=64 \
--use_focus=True --n_mixture=3 --decoding=greedy
2) Abstract Summrization
python train.py --task=SM --model=PG --load_glove=False --data=cnndm \
--rnn=LSTM --dec_hidden_size=512 \
--batch_size=16 --eval_batch_size=64 \
--use_focus=True --n_mixture=3 --decoding=greedy
--load_ckpt (integer; 5 for example)
and --eval_only
options need to be added.
1) Question Generation
python evaluate.py --task=QG --model=NQG --load_glove=True --feature_rich --data=squad \
--rnn=GRU --dec_hidden_size=512 --dropout=0.5 \
--batch_size=64 --eval_batch_size=64 \
--use_focus=True --n_mixture=3 --decoding=greedy \
--load_ckpt=5 --eval_only
2) Abstract Summrization
python evaluate.py --task=SM --model=PG --load_glove=False --data=cnndm \
--rnn=LSTM --dec_hidden_size=512 \
--batch_size=16 --eval_batch_size=64 \
--use_focus=True --n_mixture=3 --decoding=greedy \
--load_ckpt=5 --eval_only
If you use this code or model as part of any published research, please refer the following paper.
@inproceedings{cho2019focus,
title = {Mixture Content Selection for Diverse Sequence Generation},
author = {Cho, Jaemin and Seo, Minjoon and Hajishirzi, Hannaneh},
booktitle = {EMNLP},
year = {2019}
}
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