UMS for Multi-turn Response Selection in PyTorch

UMS for Multi-turn Response Selection

PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection.

@inproceedings{whang2021ums,
  title={Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection},
  author={Whang, Taesun and Lee, Dongyub and Oh, Dongsuk and Lee, Chanhee and Han, Kijong and Lee, Dong-hun and Lee, Saebyeok},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2021}
}

This code is reimplemented as a fork of huggingface/transformers and taesunwhang/BERT-ResSel.

Setup and Dependencies

This code is implemented using PyTorch v1.6.0, and provides out of the box support with CUDA 10.1 and CuDNN 7.6.5.

Anaconda / Miniconda is the recommended to set up this codebase.

Anaconda or Miniconda

Clone this repository and create an environment:

git clone https://www.github.com/taesunwhang/UMS-ResSel
conda create -n ums_ressel python=3.7

# activate the environment and install all dependencies
conda activate ums_ressel
cd UMS-ResSel

# https://pytorch.org
pip install torch==1.6.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Preparing Data and Checkpoints

Pre- and Post-trained Checkpoints

We provide following pre- and post-trained checkpoints.

• bert-base (english), bert-base-wwm (chinese)
• bert-post (ubuntu, douban, e-commerce)
• electra-base (english), electra-base (chinese)
• electra-post (ubuntu, douban, e-commerce)

sh scripts/download_pretrained_checkpoints.sh

Data pkls for Fine-tuning (Response Selection)

Original version for each dataset is availble in Ubuntu Corpus V1, Douban Corpus, and E-Commerce Corpus, respectively.

sh scripts/download_datasets.sh

Domain-specific Post-Training

Post-training Creation

Data for post-training BERT

#Ubuntu Corpus V1
sh scripts/create_bert_post_data_creation_ubuntu.sh
#Douban Corpus
sh scripts/create_bert_post_data_creation_douban.sh
#E-commerce Corpus
sh scripts/create_bert_post_data_creation_e-commerce.sh

Data for post-training ELECTRA

sh scripts/download_electra_post_training_pkl.sh

Post-training Examples

BERT+ (e.g., Ubuntu Corpus V1)

python3 main.py --model bert_post_training --task_name ubuntu --data_dir data/ubuntu_corpus_v1 --bert_pretrained bert-base-uncased --bert_checkpoint_path bert-base-uncased-pytorch_model.bin --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir --training_type post_training

ELECTRA+ (e.g., Douban Corpus)

python3 main.py --model electra_post_training --task_name douban --data_dir data/electra_post_training --bert_pretrained electra-base-chinese --bert_checkpoint_path electra-base-chinese-pytorch_model.bin --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir --training_type post_training

Training Response Selection Models

Model Arguments

BERT-Base

BERT-Post

ELECTRA-Base

ELECTRA-Post

Fine-tuning Examples

BERT+ (e.g., Ubuntu Corpus V1)

python3 main.py --model bert_post --task_name ubuntu --data_dir data/ubuntu_corpus_v1 --bert_pretrained bert-post-uncased --bert_checkpoint_path bert-post-uncased-pytorch_model.pth --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir

UMS BERT+ (e.g., Douban Corpus)

python3 main.py --model bert_post --task_name douban --data_dir data/douban --bert_pretrained bert-post-douban --bert_checkpoint_path bert-post-douban-pytorch_model.pth --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir --multi_task_type "ins,del,srch"

UMS ELECTRA (e.g., E-Commerce)

python3 main.py --model electra_base --task_name e-commerce --data_dir data/e-commerce --bert_pretrained electra-base-chinese --bert_checkpoint_path electra-base-chinese-pytorch_model.bin --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir --multi_task_type "ins,del,srch"

Evaluation

To evaluate the model, set --evaluate to /path/to/checkpoints

UMS BERT+ (e.g., Ubuntu Corpus V1)

python3 main.py --model bert_post --task_name ubuntu --data_dir data/ubuntu_corpus_v1 --bert_pretrained bert-post-uncased --bert_checkpoint_path bert-post-uncased-pytorch_model.pth --task_type response_selection --gpu_ids "0" --root_dir /path/to/root_dir --evaluate /path/to/checkpoints --multi_task_type "ins,del,srch"

Performance

We provide model checkpoints of UMS-BERT+, which obtained new state-of-the-art, for each dataset.

GitHub

https://github.com/taesunwhang/UMS-ResSel