Code for Data-to-text Generation by Splicing Together Nearest Neighbors.

Requirements

• pytorch >= 1.6.0
• transformers 3.3.1

Making the E2E data

The data/e2e directory already contains the E2E data (Novikova et al., 2017) in a pre-processed state. To replicate this preprocessing do the following:

• Retrieve neighbors with the following commands. These write neighbors to data/e2e/{train,val}-nes.txt.

python -u writenes.py -ne_fi data/e2e/train-src.txt -train_tgt_fi data/e2e/train-tgt.txt -out_fi data/e2e/train-nes.txt -nne 100 -cuda -e2e

python -u writenes.py -ne_fi data/e2e/train-src.txt -train_tgt_fi data/e2e/train-tgt.txt -val_src_fi data/e2e/val-src.txt -out_fi data/e2e/val-nes.txt -nne 100 -cuda -e2e

python -u writenes.py -ne_fi data/e2e/train-src.txt -train_tgt_fi data/e2e/train-tgt.txt -val_src_fi data/e2e/test-src.txt -out_fi data/e2e/test-nes.txt -nne 100 -cuda -e2e

• Before calculating the derivations from neighbors we mask out tokens that appear in both the source table and the target text sequence:

python -u mask_tgts.py -data data/e2e/ -src_fi data/e2e/train-src.txt -tgt_fi data/e2e/train-tgt.txt -e2e -split_dashes > data/e2e/masked-train-tgt.txt

• Finally calculate derivations:

python -u gold_derivs.py -val_src_fi data/e2e/train-src.txt -val_tgt_fi data/e2e/train-tgt.txt -val_ne_fi data/e2e/train-nes.txt -ne_tgt_fi data/e2e/masked-train-tgt.txt -out_fi data/e2e/train-encl-derivs.dat -split_dashes -nne 20 -e2e -max_srclen 100 -max_tgtlen 70 -enclose

python -u gold_derivs.py -val_src_fi data/e2e/val-src.txt -val_tgt_fi data/e2e/val-tgt.txt -val_ne_fi data/e2e/val-nes.txt -ne_tgt_fi data/e2e/masked-train-tgt.txt -out_fi data/e2e/val-encl-derivs.dat -split_dashes -nne 20 -e2e -max_srclen 100 -max_tgtlen 70 -enclose -val

Making the WikiBio data

The WikiBio data (Lebret et al., 2016) can be downloaded here. Follow the instructions at the preceding link to obtain aligned source (i.e., *.box) and target files. (You can also get the data from HuggingFace). Call the *.box files {train|val|test}-src.txt and the target files {train|val|test}-tgt.txt and put them in the same directory, say, data/wb/.

Below we detail the commands necessary for calculating nearest neighbors and oracle derivations. These files can also be downloaded from this folder and they should be placed in the same directory as the {train|val|test}-src.txt and {train|val|test}-tgt.txt files (data/wb/ in the scripts below).

• Retrieve neighbors (you may want to increase or decrease -bsz in the script below depending your hardware):

python -u writenes.py -ne_fi data/wb/train-src.txt -train_tgt_fi data/wb/train-tgt.txt -out_fi data/wb/train-nes.txt -nne 40 -cuda

python -u writenes.py -ne_fi data/wb/train-src.txt -train_tgt_fi data/wb/train-tgt.txt -val_src_fi data/wb/val-src.txt -out_fi data/wb/val-nes.txt -nne 40 -cuda

python -u writenes.py -ne_fi data/wb/train-src.txt -train_tgt_fi data/wb/train-tgt.txt -val_src_fi data/wb/test-src.txt -out_fi data/wb/test-nes.txt -nne 40 -cuda

• Mask target tokens appearing in the source table:

python -u mask_tgts.py -data data/wb/ -src_fi data/wb/train-src.txt -tgt_fi data/wb/train-tgt.txt -split_dashes > data/wb/masked-train-tgt.txt

• Calculate derivations. This can also be parallelized using the -wrkr option (e.g., 2 processes might use arguments -wrkr 1,2, -wrkr 2,2 respectively) and then all the separately produced derivations can be collected with collect_derivations.py

python -u gold_derivs.py -val_src_fi data/wb/train-src.txt -val_tgt_fi data/wb/train-tgt.txt -val_ne_fi data/wb/train-nes.txt -ne_tgt_fi data/wb/masked-train-tgt.txt -out_fi data/wb/train-encl-derivs.dat -split_dashes -nne 20 -max_srclen 130 -max_tgtlen 50 -enclose

python -u gold_derivs.py -val_src_fi data/wb/val-src.txt -val_tgt_fi data/wb/val-tgt.txt -val_ne_fi data/wb/val-nes.txt -ne_tgt_fi data/wb/masked-train-tgt.txt -out_fi data/wb/val-encl-derivs.dat -split_dashes -nne 20 -max_srclen 130 -max_tgtlen 50 -enclose -val

Training

python -u train.py -cuda -data data/e2e -adamhyps 0.9,0.999,1e-7,0.001 -Topts CNSWx2 -embdim 420 -enc_layers 6 -epochs 100 -ffdim 650 -init 0.01 -lr 0.001 -min_nes 20 -min_seq_accum 400 -min_valnes 20 -nheads 7 -nne 20 -optalg adamw -seed 3636 -senc_layers 6 -share_encs -src_mode mask -val_bsz 16 -val_mbs_per_epoch 300 -bsz 8 -mbs_per_epoch 5260 -log_interval 1000 -flat_moves -vocopts 20 20 -1 -1 -drop 0.1 -warmup_steps 4000 -split_dashes -enclose -save my_e2e_model.pt

python -u train.py -cuda -data data/wb -adamhyps 0.9,0.999,1e-7,0.001 -Topts CNSWx2 -embdim 420 -enc_layers 6 -epochs 100 -ffdim 650 -init 0.01 -lr 0.0005 -min_nes 20 -min_seq_accum 400 -min_valnes 20 -nheads 7 -nne 20 -optalg adamw -seed 3636 -senc_layers 6 -share_encs -src_mode mask -val_bsz 16 -val_mbs_per_epoch 850 -bsz 6 -mbs_per_epoch 48000 -log_interval 1000 -flat_moves -vocopts 50 50 -1 -1 -drop 0.1 -warmup_steps 4000 -split_dashes -enclose -save my_wb_model.pt

Generation

python -u search.py -cuda -data data/e2e/ -val_src_fi src_uniq_valid.txt -val_nefi uniqval-nes.txt -get_trace -bsz 1 -train_from my_e2e_model.pt-l -split_dashes -K 5 -max_moves 16 -nne 20 -out_fi e2e_val.out

python -u search.py -cuda -data data/wb/ -val_src_fi val-src.txt -val_nefi val-nes.txt -get_trace -bsz 1 -train_from my_wb_model.pt-l -split_dashes -K 10 -max_moves 28 -nne 20 -out_fi wb_val.out

Postprocessing

The generated text comes enclosed in tags and along with its derivation. To obtain just the generated text, run

python strip_tags_and_deriv.py < generated_text.out > generated_text.strpd

The eval_e2e_out.bash calls the above, as well as the detokenization script from the TGen repo, before calling the standard E2E evaluation script from the E2E metrics repo. The eval_e2e_out.bash script should be run like:

bash eval_e2e_out.bash e2e_preds.out gold_target_file.txt