Code for Challenges in Data-to-Document Generation (Wiseman, Shieber, Rush; EMNLP 2017); much of this code is adapted from an earlier fork of OpenNMT.
The boxscore-data associated with the above paper can be downloaded from the boxscore-data repo, and this README will go over running experiments on the RotoWire portion of the data; running on the SBNation data (or other data) is quite similar.
Update 2: For an improved implementation of the extractive evaluation metrics (and improved models), please see the data2text-plan-py repo associated with the Puduppully et al. (AAAI 2019) paper.
Update: models and results reflecting the newly cleaned up data in the boxscore-data repo are now given below.
Before training models, you must preprocess the data. Assuming the RotoWire json files reside at ~/Documents/code/boxscore-data/rotowire, the following command will preprocess the data
th box_preprocess.lua -json_data_dir ~/Documents/code/boxscore-data/rotowire -save_data roto
and write files called roto-train.t7, roto.src.dict, and roto.tgt.dict to your local directory.
For the "conditional copy" model, it is necessary to know where in the source table each target word may have been copied from.
This pointer information can be incorporated into the preprocessing by running:
th box_preprocess.lua -json_data_dir ~/Documents/code/boxscore-data/rotowire -save_data roto -ptr_fi "roto-ptrs.txt"
The file roto-ptrs.txt has been included in the repo.
The command for training the Joint Copy + Rec + TVD model is as follows:
th box_train.lua -data roto-train.t7 -save_model roto_jc_rec_tvd -rnn_size 600 -word_vec_size 600 -enc_emb_size 600 -max_batch_size 16 -dropout 0.5 -feat_merge concat -pool mean -enc_layers 1 -enc_relu -report_every 50 -gpuid 1 -epochs 50 -learning_rate 1 -enc_dropout 0 -decay_update2 -layers 2 -copy_generate -tanh_query -max_bptt 100 -discrec -rho 1 -partition_feats -recembsize 600 -discdist 1 -seed 0
A model trained in this way can be downloaded from https://drive.google.com/file/d/0B1ytQXPDuw7ONlZOQ2R3UWxmZ2s/view?usp=sharing
An updated model can be downloaded from https://drive.google.com/drive/folders/1QKudbCwFuj1BAhpY58JstyGLZXvZ-2w-?usp=sharing
The command for training the Conditional Copy model is as follows:
th box_train.lua -data roto-train.t7 -save_model roto_cc -rnn_size 600 -word_vec_size 600 -enc_emb_size 600 -max_batch_size 16 -dropout 0.5 -feat_merge concat -pool mean -enc_layers 1 -enc_relu -report_every 50 -gpuid 1 -epochs 100 -learning_rate 1 -enc_dropout 0 -decay_update2 -layers 2 -copy_generate -tanh_query -max_bptt 100 -switch -multilabel -seed 0
A model trained in this way can be downloaded from https://drive.google.com/file/d/0B1ytQXPDuw7OaHZJZjVWd2N6R2M/view?usp=sharing
An updated model can be downloaded from https://drive.google.com/drive/folders/1QKudbCwFuj1BAhpY58JstyGLZXvZ-2w-?usp=sharing
Use the following commands to generate from the above models:
th box_train.lua -data roto-train.t7 -save_model roto_jc_rec_tvd -rnn_size 600 -word_vec_size 600 -enc_emb_size 600 -max_batch_size 16 -dropout 0.5 -feat_merge concat -pool mean -enc_layers 1 -enc_relu -report_every 50 -gpuid 1 -epochs 50 -learning_rate 1 -enc_dropout 0 -decay_update2 -layers 2 -copy_generate -tanh_query -max_bptt 100 -discrec -rho 1 -partition_feats -recembsize 600 -discdist 1 -train_from roto_jc_rec_tvd_epoch45_7.22.t7 -just_gen -beam_size 5 -gen_file roto_jc_rec_tvd-beam5_gens.txt
th box_train.lua -data roto-train.t7 -save_model roto_cc -rnn_size 600 -word_vec_size 600 -enc_emb_size 600 -max_batch_size 16 -dropout 0.5 -feat_merge concat -pool mean -enc_layers 1 -enc_relu -report_every 50 -gpuid 1 -epochs 100 -learning_rate 1 -enc_dropout 0 -decay_update2 -layers 2 -copy_generate -tanh_query -max_bptt 100 -switch -multilabel -train_from roto_cc_epoch34_7.44.t7 -just_gen -beam_size 5 -gen_file roto_cc-beam5_gens.txt
The beam size used in generation can be adjusted with the -beam_size argument. You can generate on the test data by supplying the -test flag.
You can regenerate a pointer file with
python data_utils.py -mode ptrs -input_path ~/Documents/code/boxscore-data/rotowire/train.json -output_fi "my-roto-ptrs.txt"
You can create a dataset for training or evaluating the relation extraction system as follows:
python data_utils.py -mode make_ie_data -input_path "../boxscore-data/rotowire" -output_fi "roto-ie.h5"
This will create files roto-ie.h5, roto-ie.dict, and roto-ie.labels.
- You can download the extraction models we ensemble to do the evaluation from this link. There are six models in total, with the name pattern
*ie-ep*.t7. Put these extraction models in the same directory asextractor.lua. (Note thatextractor.luahard-codes the paths to these saved models, so you'll need to change this if you want to substitute in new models.)
Updated extraction models can be downloaded from https://drive.google.com/drive/folders/1QKudbCwFuj1BAhpY58JstyGLZXvZ-2w-?usp=sharing
- Once you've generated summaries, you can put them into a format the extraction system can consume as follows:
python data_utils.py -mode prep_gen_data -gen_fi roto_cc-beam5_gens.txt -dict_pfx "roto-ie" -output_fi roto_cc-beam5_gens.h5 -input_path "../boxscore-data/rotowire"
where the file you've generated is called roto_cc-beam5_gens.txt and the dictionary and labels files are in roto-ie.dict and roto-ie.labels respectively (as above). This will create a file called roto_cc-beam5_gens.h5, which can be consumed by the extraction system.
- The extraction system can then be run as follows:
th extractor.lua -gpuid 1 -datafile roto-ie.h5 -preddata roto_cc-beam5_gens.h5 -dict_pfx "roto-ie" -just_eval
This will print out the RG metric numbers. (For the recall number, divide the 'nodup correct' number by the total number of generated summaries, e.g., 727). It will also generate a file called roto_cc-beam5_gens.h5-tuples.txt, which contains the extracted relations, which can be compared to the gold extracted relations.
-
We now need the tuples from the gold summaries.
roto-gold-val.h5-tuples.txtandroto-gold-test.h5-tuples.txthave been included in the repo, but they can be recreated by repeating steps 2 and 3 using the gold summaries (with one gold summary per-line, as usual). -
The remaining metrics can now be obtained by running:
python non_rg_metrics.py roto-gold-val.h5-tuples.txt roto_cc-beam5_gens.h5-tuples.txt
I trained the convolutional IE model as follows:
th extractor.lua -gpuid 1 -datafile roto-ie.h5 -lr 0.7 -embed_size 200 -conv_fc_layer_size 500 -dropout 0.5 -savefile roto-convie
I trained the BLSTM IE model as follows:
th extractor.lua -gpuid 1 -datafile roto-ie.h5 -lstm -lr 1 -embed_size 200 -blstm_fc_layer_size 700 -dropout 0.5 -savefile roto-blstmie -seed 1111
The saved models linked to above were obtained by varying the seed or the epoch.
On the development set:
| RG (P% / #) | CS (P% / R%) | CO | PPL | BLEU | |
|---|---|---|---|---|---|
| Gold | 95.98 / 16.93 | 100 / 100 | 100 | 1 | 100 |
| Template | 99.93 / 54.21 | 23.42 / 72.62 | 11.30 | N/A | 8.97 |
| Joint+Rec+TVD (B=1) | 61.23 / 15.27 | 28.79 / 39.80 | 15.27 | 7.26 | 12.69 |
| Conditional (B=1) | 76.66 / 12.88 | 37.98 / 35.46 | 16.70 | 7.29 | 13.60 |
| Joint+Rec+TVD (B=5) | 62.84 / 16.77 | 27.23 / 40.60 | 14.47 | 7.26 | 13.44 |
| Conditional (B=5) | 75.74 / 16.93 | 31.20 / 38.94 | 14.98 | 7.29 | 14.57 |
On the test set:
| RG (P% / #) | CS (P% / R%) | CO | PPL | BLEU | |
|---|---|---|---|---|---|
| Gold | 96.11 / 17.31 | 100 / 100 | 100 | 1 | 100 |
| Template | 99.95 / 54.15 | 23.74 / 72.36 | 11.68 | N/A | 8.93 |
| Joint+Rec+TVD (B=5) | 62.66 / 16.82 | 27.60 / 40.59 | 14.57 | 7.49 | 13.61 |
| Conditional (B=5) | 75.62 / 16.83 | 32.80 / 39.93 | 15.62 | 7.53 | 14.19 |
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