Focused Attention Improves Document Grounded Generation

Code accompanying the paper: Focused Attention Improves Document Grounded Generation

Pre-requisites

transformers 3.1.0
NLG-Eval

Data

Wikipedia Update Generation Task

Download the raw data from this link. The raw files have names train_info.csv, valid_info.csv, test_info.csv. Use the scripts/prepare_wiki_update_gen.py script to prepare the data in the appropriate format with the command:

python scripts/prepare_wiki_update_gen.py --data_dir raw_data/ --out_dir data/wiki_update_gen/

CMU DoG Task

Download the data from this link. Use the scripts/prepare_cmu_dog.py script to prepare the data in the appropriate format with the command:

python scripts/prepare_cmu_dog.py --data_dir datasets-CMU_DoG/ --out_dir data/cmu_dog/

Quickstart

BART Baseline

Use the run_train.py to train and test the BART baseline.

Train the BART model using the following command:

python run_train.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_train --output_dir trained_models/wiki_update_gen/bart/ --log_file_path trained_models/wiki_update_gen/bart/log.txt --source_max_len 1024 --target_max_len 128

Run the trained Bart model on the test set. This script creates two files predictions.txt and reference.txt, and saves it in the data_sir path provided.

python run_train.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_generate --output_dir trained_models/wiki_update_gen/bart/checkpoint-X/ --model_file_path trained_models/wiki_update_gen/bart/checkpoint-X/model.pt --source_max_len 1024 --target_max_len 128

CoDR Model

Use the codr.py to train and test the CoDR baseline.

Train the CoDR model using the following command:

python codr.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_train --output_dir trained_models/wiki_update_gen/codr/ --log_file_path trained_models/wiki_update_gen/codr/log.txt --source_max_len 1024 --target_max_len 128 --learning_rate 2e-5

Run the trained CoDR model on the test set. This script creates two files predictions.txt and reference.txt, and saves it in the data_sir path provided.

python codr.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_generate --output_dir trained_models/wiki_update_gen/codr/checkpoint-X/ --model_file_path trained_models/wiki_update_gen/codr/checkpoint-X/model.pt --source_max_len 1024 --target_max_len 128

DoHA Model

Use the doha.py to train and test the DoHA baseline.

Important Note: You have to copy the patch provided in patch folder to the desired location by running the apply_patch.sh script (You have to change the path where to copy this file). Find out the path where the transformers library is installed and replace the original generation_utils.py file in the transformers library with the patch/generation_utils.py file.

Train the DoHA model using the following command:

python doha.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_train --output_dir trained_models/wiki_update_gen/doha/ --log_file_path trained_models/wiki_update_gen/doha/log.txt --source_max_len 1024 --target_max_len 128 --learning_rate 2e-5

Run the trained DoHA model on the test set. This script creates two files predictions.txt and reference.txt, and saves it in the data_sir path provided.

python doha.py --data_dir data/wiki_update_gen/ --experiment_type 'chat_document' --do_generate --output_dir trained_models/wiki_update_gen/doha/checkpoint-X/ --model_file_path trained_models/wiki_update_gen/doha/checkpoint-X/model.pt --source_max_len 1024 --target_max_len 128

Evaluation

nlg-eval --hypothesis=trained_models/wiki_update_gen/bart/checkpoint-X/predictions.txt --references=trained_models/wiki_update_gen/bart/checkpoint-X/reference.txt --no-skipthoughts --no-glove

Trained Models

Download all the trained models from the links below. In each case, you will find three folders corresponding to the bart, codr and doha models, containing model.pt file.

http://tts.speech.cs.cmu.edu/document_grounded_generation/cmu_dog/cmu_dog.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/wikipedia_update_gen/wikipedia_update_gen.zip

Alternatively, if you are only interested in specific trained models, then you download the desired model from the links below:

http://tts.speech.cs.cmu.edu/document_grounded_generation/cmu_dog/cmu_dog_bart.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/cmu_dog/cmu_dog_codr.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/cmu_dog/cmu_dog_doha.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/wikipedia_update_gen/wikipedia_update_gen_bart.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/wikipedia_update_gen/wikipedia_update_gen_codr.zip
http://tts.speech.cs.cmu.edu/document_grounded_generation/wikipedia_update_gen/wikipedia_update_gen_doha.zip

Contributors

If you use this code please cite the following:

@inproceedings{prabhumoye-etal-2021-focused,
  title={Focused Attention Improves Document Grounded Generation},
  author={Prabhumoye, Shrimai and Hashimoto, Kazuma and Zhou, Yingbo and Black, Alan W and Salakhutdinov, Ruslan},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics},
  publisher={Association for Computational Linguistics},
  year={2021},
  }

shrimai / Focused-Attention-Improves-Document-Grounded-Generation

readme