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Code for AAAI 2019 paper on Data-to-Text Generation with Content Selection and Planning

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This repo contains code for Data-to-Text Generation with Content Selection and Planning (Puduppully, R., Dong, L., & Lapata, M.; AAAI 2019); this code is based on an earlier fork of OpenNMT-py. The Pytorch version is 0.3.1.

Update: For a model with better relation generation precision (RG P%) and other metrics, please see the macro planning repository and the corresponding TACL 2021 paper.


  author    = {Ratish Puduppully and
               Li Dong and
               Mirella Lapata},
  title     = {Data-to-Text Generation with Content Selection and Planning},
  booktitle = {The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI}
               2019, The Thirty-First Innovative Applications of Artificial Intelligence
               Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
               Advances in Artificial Intelligence, {EAAI} 2019, Honolulu, Hawaii,
               USA, January 27 - February 1, 2019},
  pages     = {6908--6915},
  publisher = {{AAAI} Press},
  year      = {2019},
  url       = {},
  doi       = {10.1609/aaai.v33i01.33016908},
  timestamp = {Tue, 02 Feb 2021 08:00:48 +0100},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

Test set output

The test set output for the model can be found here


All dependencies can be installed via:

pip install -r requirements.txt

Note that the Pytorch version is 0.3.1 and Python version is 2.7. The path to Pytorch wheel in requirements.txt is configured with CUDA 8.0. You may change it to the desired CUDA version.


The boxscore-data json files can be downloaded from the boxscore-data repo.

The input dataset for data2text-plan-py can be created by running the script in scripts folder. The dataset so obtained is available at link


Assuming the OpenNMT-py input files reside at ~/boxscore-data, the following command will preprocess the data


mkdir $BASE/preprocess
python -train_src1 $BASE/rotowire/src_train.txt -train_tgt1 $BASE/rotowire/train_content_plan.txt -train_src2 $BASE/rotowire/inter/train_content_plan.txt -train_tgt2 $BASE/rotowire/tgt_train.txt -valid_src1 $BASE/rotowire/src_valid.txt -valid_tgt1 $BASE/rotowire/valid_content_plan.txt -valid_src2 $BASE/rotowire/inter/valid_content_plan.txt -valid_tgt2 $BASE/rotowire/tgt_valid.txt -save_data $BASE/preprocess/roto -src_seq_length 1000 -tgt_seq_length 1000 -dynamic_dict -train_ptr $BASE/rotowire/train-roto-ptrs.txt

The train-roto-ptrs.txt file is available along with the dataset and can also be created by the following command

python -mode ptrs -input_path $BASE/rotowire/train.json -train_content_plan $BASE/rotowire/inter/train_content_plan.txt -output_fi $BASE/rotowire/train-roto-ptrs.txt

Training (and Downloading Trained Models)

The command for training the Neural Content Planning model with conditional copy NCP+CC is as follows:


python -data $BASE/preprocess/roto -save_model $BASE/gen_model/$IDENTIFIER/roto -encoder_type1 mean -decoder_type1 pointer -enc_layers1 1 -dec_layers1 1 -encoder_type2 brnn -decoder_type2 rnn -enc_layers2 2 -dec_layers2 2 -batch_size 5 -feat_merge mlp -feat_vec_size 600 -word_vec_size 600 -rnn_size 600 -seed 1234 -start_checkpoint_at 4 -epochs 25 -optim adagrad -learning_rate 0.15 -adagrad_accumulator_init 0.1 -report_every 100 -copy_attn -truncated_decoder 100 -gpuid $GPUID -attn_hidden 64 -reuse_copy_attn -start_decay_at 4 -learning_rate_decay 0.97 -valid_batch_size 5

The NCP+CC model can be downloaded from


During inference, we first generate the content plan

MODEL_PATH=<path to model1>

python -model $MODEL_PATH -src1 $BASE/rotowire/inf_src_valid.txt -output $BASE/gen/roto_stage1_$IDENTIFIER-beam5_gens.txt -batch_size 10 -max_length 80 -gpu $GPUID -min_length 35 -stage1 

This script generates the content plan with records from input of content plan with indices

python scripts/ $BASE/rotowire/inf_src_valid.txt $BASE/gen/roto_stage1_$IDENTIFIER-beam5_gens.txt $BASE/transform_gen/roto_stage1_$IDENTIFIER-beam5_gens.h5-tuples.txt  $BASE/gen/roto_stage1_inter_$IDENTIFIER-beam5_gens.txt

The accuracy of content plan in first stage can be evaluated using the following command

python $BASE/transform_gen/roto-gold-val-beam5_gens.h5-tuples.txt $BASE/transform_gen/roto_stage1_$IDENTIFIER-beam5_gens.h5-tuples.txt 

The output summary is generated using the command

MODEL_PATH2=<path to model2>

python -model $MODEL_PATH -model2 $MODEL_PATH2 -src1 $BASE/rotowire/inf_src_valid.txt -tgt1 $BASE/gen/roto_stage1_$IDENTIFIER-beam5_gens.txt -src2 $BASE/gen/roto_stage1_inter_$IDENTIFIER-beam5_gens.txt -output $BASE/gen/roto_stage2_$IDENTIFIER-beam5_gens.txt -batch_size 10 -max_length 850 -min_length 150 -gpu $GPUID

Automatic evaluation using IE metrics

Metrics of RG, CS, CO are computed using the below commands.

python -mode prep_gen_data -gen_fi $BASE/gen/roto_stage2_$IDENTIFIER-beam5_gens.txt -dict_pfx "roto-ie" -output_fi $BASE/transform_gen/roto_stage2_$IDENTIFIER-beam5_gens.h5 -input_path "/boxcore-json/rotowire"

th extractor.lua -gpuid  $GPUID -datafile roto-ie.h5 -preddata $BASE/transform_gen/roto_stage2_$IDENTIFIER-beam5_gens.h5 -dict_pfx "roto-ie" -just_eval

python $BASE/transform_gen/roto-gold-val-beam5_gens.h5-tuples.txt $BASE/transform_gen/roto_stage2_$IDENTIFIER-beam5_gens.h5-tuples.txt 

Evaluation using BLEU script

The BLEU perl script can be obtained from Command to compute BLEU score:

~/multi-bleu.perl $BASE/rotowire/inf_tgt_valid.txt < $BASE/gen/roto_stage2_$IDENTIFIER-beam5_gens.txt

IE models

For training the IE models, follow the updated code in which contains bug fixes for number handling. The repo contains the downloadable links for IE models too.


Code for AAAI 2019 paper on Data-to-Text Generation with Content Selection and Planning






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