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Fine-grained Post-training for Improving Retrieval-based Dialogue Systems - NAACL 2021

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Fine-grained Post-training for Multi-turn Response Selection

PWC

Implements the model described in the following paper Fine-grained Post-training for Improving Retrieval-based Dialogue Systems in NAACL-2021.

@inproceedings{han-etal-2021-fine,
title = "Fine-grained Post-training for Improving Retrieval-based Dialogue Systems",
author = "Han, Janghoon  and Hong, Taesuk  and Kim, Byoungjae  and Ko, Youngjoong  and Seo, Jungyun",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.naacl-main.122", pages = "1549--1558",
}

This code is reimplemented as a fork of huggingface/transformers.

alt text

Setup and Dependencies

This code is implemented using PyTorch v1.8.0, and provides out of the box support with CUDA 11.2 Anaconda is the recommended to set up this codebase.

# https://pytorch.org
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install -r requirements.txt

Preparing Data and Checkpoints

Post-trained and fine-tuned Checkpoints

We provide following post-trained and fine-tuned checkpoints.

Data pkl for Fine-tuning (Response Selection)

We used the following data for post-training and fine-tuning

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

Fine-grained Post-Training

Making Data for post-training and fine-tuning
Data_processing.py

Post-training Examples

(Ubuntu Corpus V1, Douban Corpus, E-commerce Corpus)
python -u FPT/ubuntu_final.py --num_train_epochs 25
python -u FPT/douban_final.py --num_train_epochs 27
python -u FPT/e_commmerce_final.py --num_train_epochs 34

Fine-tuning Examples

(Ubuntu Corpus V1, Douban Corpus, E-commerce Corpus)
Taining
To train the model, set `--is_training`
python -u Fine-Tuning/Response_selection.py --task ubuntu --is_training
python -u Fine-Tuning/Response_selection.py --task douban --is_training
python -u Fine-Tuning/Response_selection.py --task e_commerce --is_training
Testing
python -u Fine-Tuning/Response_selection.py --task ubuntu
python -u Fine-Tuning/Response_selection.py --task douban 
python -u Fine-Tuning/Response_selection.py --task e_commerce

Training Response Selection Models

Model Arguments

Fine-grained post-training
task_name data_dir checkpoint_path
ubuntu ubuntu_data/ubuntu_post_train.pkl FPT/PT_checkpoint/ubuntu/bert.pt
douban douban_data/douban_post_train.pkl FPT/PT_checkpoint/douban/bert.pt
e-commerce e_commerce_data/e_commerce_post_train.pkl FPT/PT_checkpoint/e_commerce/bert.pt
Fine-tuning
task_name data_dir checkpoint_path
ubuntu ubuntu_data/ubuntu_dataset_1M.pkl Fine-Tuning/FT_checkpoint/ubuntu.0.pt
douban douban_data/douban_dataset_1M.pkl Fine-Tuning/FT_checkpoint/douban.0.pt
e-commerce e_commerce_data/e_commerce_dataset_1M.pkl Fine-Tuning/FT_checkpoint/e_commerce.0.pt

Performance

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

Ubuntu R@1 R@2 R@5
[BERT_FP] 0.911 0.962 0.994
Douban MAP MRR P@1 R@1 R@2 R@5
[BERT_FP] 0.644 0.680 0.512 0.324 0.542 0.870
E-Commerce R@1 R@2 R@5
[BERT_FP] 0.870 0.956 0.993

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