data2text-plan-py

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.

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 create_dataset.py in scripts folder. The dataset so obtained is available at link https://drive.google.com/open?id=1R_82ifGiybHKuXnVnC8JhBTW8BAkdwek

Preprocessing

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

BASE=~/boxscore-data
IDENTIFIER=cc

python preprocess.py -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 data_utils.py -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:

BASE=~/boxscore-data
IDENTIFIER=cc

python train.py -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 https://www.dropbox.com/sh/vo5wb2fuq7m0bk0/AABikW0KomOKIor24wD8VSFWa?dl=0

Generation

During inference, we first generate the content plan

MODEL_PATH=<path to model1>

python translate.py -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/create_content_plan_from_index.py $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 non_rg_metrics.py $BASE/transform_gen/roto_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 translate.py -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 data_utils.py -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 non_rg_metrics.py $BASE/transform_gen/roto_val-beam5_gens.h5-tuples.txt $BASE/transform_gen/roto_stage2_$IDENTIFIER-beam5_gens.h5-tuples.txt

IE models

For training the IE models, follow the updated code in https://github.com/ratishsp/data2text-1 which contains bug fixes for number handling. The repo contains the downloadable links for IE models too.

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