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GODEL: Large-Scale Pre-Training for Goal-Directed Dialog

News

(Update 10/23/2022) We have released GODEL V1.1, which is trained on 551M multi-turn dialogs from Reddit discussion thread, and 5M instruction and knowledge grounded dialogs. It has shown significantly better results on our benchmark, especially in the zero-shot setting.

Please check out our model cards in the huggingface Transformers repository. With several lines of code, it should be pretty straightforward to chat with GODEL. A live demo is shown here.

Base model: https://huggingface.co/microsoft/GODEL-v1_1-base-seq2seq

Large model: https://huggingface.co/microsoft/GODEL-v1_1-large-seq2seq

Introduction

This repository showcases building goal-directed dialog using GODEL, and contains the dataset, source code and pre-trained model for the following paper:

GODEL: Large-Scale Pre-Training for Goal-Directed Dialog<br>Baolin Peng, Michel Galley, Pengcheng He, Chris Brockett, Lars Liden, Elnaz Nouri, Zhou Yu, Bill Dolan, Jianfeng Gao

GODEL is a large-scale pre-trained model for goal-directed dialogs. It is parameterized with a Transformer-based encoder-decoder model and trained for response generation grounded in external text, which allows more effective fine-tuning on dialog tasks that require conditioning the response on information that is external to the current conversation (e.g., a retrieved document). The pre-trained model can be efficiently fine-tuned and adapted to accomplish a new dialog task with a handful of task-specific dialogs.

This repository is based on Hugginface Transformers. Some evaluation scripts and dataset are adapted from DSTC7-End-to-End-Conversation-Modeling, DialoGPT, UnifiedQA, MS MARCO, MultiWOZ, Schema-Guided Dataset, etc.

The included scripts can be used to reproduce the results reported in the paper. Project and demo webpage: https://aka.ms/GODEL

Installation

Requires The interactive interface requries node.js and npm. Please refer to here for installation.

Please use the below commands to create the environment, clone the repo and install required packages.

conda create -n godel-env python=3.8
conda activate godel-env
conda install nodejs
git clone https://github.com/microsoft/GODEL.git
cd GODEL
pip install -r requirements.txt
export PYTHONPATH="`pwd`"

Fetch and unzip the pretrained model based on which to continue finetune your own data.

wget https://bapengstorage.blob.core.windows.net/fileshare/godel_base.tar.gz
tar -zxvf godel_base.tar.gz

Pipeline

Data format

  {
    "Context": "Please remind me of calling to Jessie at 2PM.",
    "Knowledge": "reminder_contact_name is Jessie, reminder_time is 2PM",
    "Response": "Sure, set the reminder: call to Jesse at 2PM"
  },

We use json format to represent a training example. As shown in the above example, it contains the following fields:

• Context - The context from session beginning to current turn.
• Knowledge - External or environment state represented in plain text.
• Reponse - The target agent respose. It can be a template, an api call or natural language.

Fine-tuning

DATA_NAME={path_of_data}
OUTPUT_DIR={path_of_fine-tuned_model}
MODEL_PATH={path_of_pre-trained_model}
EXP_NAME={experiment_name}

python train.py --model_name_or_path ${MODEL_PATH} \
	--dataset_name ${DATA_NAME} \
	--output_dir ${OUTPUT_DIR} \
	--per_device_train_batch_size=16 \
	--per_device_eval_batch_size=16 \
	--max_target_length 512 \
	--max_length 512 \
	--num_train_epochs 50 \
	--save_steps 10000 \
	--num_beams 5 \
	--exp_name ${EXP_NAME} --preprocessing_num_workers 24

Generation

DATA_NAME={path_of_data}
OUTPUT_DIR={path_to_save_predictions}
MODEL_PATH={path_of_fine-tuned_model}

python generate.py --model_name_or_path ${MODEL_PATH}  \
	--dataset_name ${DATA_NAME}  \
	--output_dir ${OUTPUT_DIR}  \
	--per_device_eval_batch_size=16  \
	--max_target_length 128 \
	--max_length 512  \
	--preprocessing_num_workers 24  \
	--num_beams 5 

Interaction

We provide a demo interface to chat with finetuned models. The backend server is based on flask and the interface is based on vue, bootstrap-vue, and BasicVueChat.

Start the backend server:

# Please create the backend server refering to e.g., dstc9_server.py
python EXAMPLE_server.py # start the sever and expose 8080 

Start serving frontend page:

cd GODEL/html
npm install
npm run serve 

Open localhost:8080 in your web browser, you will see the following page. Note that the backend port should be consistent with the port used in html/compoents/chat.vue.

A live demo is shown here.

Models

We have released GODEL V1.1, which is trained on 551M multi-turn dialogs from Reddit discussion thread and 5M instruction and knowledge-grounded dialogs. More models will be released later.

We have released three fine-tuned models which can be further fine-tuned on low-resource user-customized dataset. The total parameters in these models range from 117M to 2.7B.

Training

5/22/2023: Pretraining GODEL models with our codebase is no longer supported, but GODEL models remain available. See here for details.

Fine-tuning and Evaluation

GODEL is fine-tuned and evaluated on four tasks. We provide scripts to create training and testing data in our format. Please refer to create_downstream_dataset.sh to download the original data and execute the following cmd.

cd scripts 
./create_downstream_dataset.sh

GROUNDED_CHECKPOINT={path_to_saved_checkpoint}
OUTPUT_DIR={path_to_save_predictions}
TASK=wow
accelerate launch --config_file configs/G16_config.yaml train.py 
	--model_name_or_path ${GROUNDED_CHECKPOINT} \
	--dataset_name ./datasets_loader/${TASK}_dataset.py \
	--output_dir ${OUTPUT_DIR} \
	--per_device_train_batch_size=16 \
	--per_device_eval_batch_size=16 \
	--max_target_length 256 \
	--max_length 512 \
	--num_train_epochs 10 \
	--preprocessing_num_workers 24 \
	--num_beams 5 \
	--exp_name ${TASK}  \
	--learning_rate 5e-5 \	
	--save_every_checkpoint \
	--save_steps 50000 

Tutorial - Adding a new task using GODEL

In this tutorial, you will build a grounded dialog model based on GODEL for DSTC9 task. Detailed information can be found at here.

Firstly download the data and convert it to GODEL format.

cd examples/dstc9
./create_data.sh

Finetune with the pre-trained GODEL model

cd GODEL 
GODEL_MODEL={path_to_pre-trained_model}
python train.py 
	--model_name_or_path ${GODEL_MODEL}   \
	--dataset_name ../examples/dstc9/dstc9_dataset.py   \
	--output_dir ../examples/dstc9/ckpt   \
	--per_device_train_batch_size=16  \
	--per_device_eval_batch_size=16  \
	--max_target_length 128  \
	--max_length 512  \
	--num_train_epochs 50  \
	--save_steps 10000  \
	--num_beams 5  \
	--exp_name wow-test \
	--preprocessing_num_workers 24 \
	--save_every_checkpoint 

Interact with above trained model

cd examples/dstc9
# replace model path in dstc9_server with a trained ckpt in line 49
python dstc9_server.py

cd GODEL/html 
npm install
npm run serve

Disclaimer

This repository aims to facilitate research in a paradigm shift of building task bots at scale. This toolkit contains only part of the modeling machinery needed to actually produce a model weight file in a running dialog. On its own, this model provides only information about the weights of various text spans; in order for a researcher to actually use it, they will need to bring in-house conversational data of their own for future pre-training and decode the response generation from the pretrained/finetuned system. Microsoft is not responsible for any generation from the 3rd party utilization of the pretrained system.

Citation

if you use this code and data in your research, please cite our arxiv paper:

@misc{peng2022godel,
author = {Peng, Baolin and Galley, Michel and He, Pengcheng and Brockett, Chris and Liden, Lars and Nouri, Elnaz and Yu, Zhou and Dolan, Bill and Gao, Jianfeng},
title = {GODEL: Large-Scale Pre-training for Goal-Directed Dialog},
howpublished = {arXiv},
year = {2022},
month = {June},
url = {https://www.microsoft.com/en-us/research/publication/godel-large-scale-pre-training-for-goal-directed-dialog/},
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.