GRTr: Generative-Retrieval Transformers

Codebase for the papers:

Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation, DSTC8@AAAI 2020

Fast Domain Adaptation for Goal-Oriented Dialogue Using a Hybrid Generative-Retrieval Transformer, ICASSP 2020 [virtual presentation]

By Igor Shalyminov, Alessandro Sordoni, Adam Atkinson, Hannes Schulz.

Installation

$ cd code-directory
$ conda create -n hybrid_retgen python=3.7
$ conda activate hybrid_retgen
$ conda install cython
$ pip install -e .

For mixed precision training:

$ pip install git+https://github.com/nvidia/apex

Datasets and experimental setup

• MetaLWOz [paper]
• baselines and task info

Training a base GPT-2 model on MetaLWOz

$ python scripts/train <MetaLWOz zipfile> metalwoz_dataspec.json --dataset_cache cache exp/grtr --train_batch_size 4 --valid_batch_size 4 --early_stopping_after -1 --n_epochs 25

Add --fp16 O1 to use mixed precision training.

Predictions

generate-and-rank

python scripts/predict_generate_and_rank <MetaLWOz/MultiWoz zipfile> <testspec json> <output dir> <base GPT-2 model dir> --fine-tune --dataset_cache cache exp/grtr --train_batch_size 4 --valid_batch_size 4

generate only

python scripts/predict <MetaLWOz/MultiWoz zipfile> <testspec json> <output dir> <base GPT-2 model dir> --fine-tune --dataset_cache cache exp/grtr --train_batch_size 4 --valid_batch_size 4

Convenience bash scripts are provided in scripts/ to produce predictions for each of the three test specs.

Evaluation can be done using the evaluate script in the competition baseline repository.

Contributing

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