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

Related tags

Deep LearningUMS-ResSel
Overview

UMS for Multi-turn Response Selection

PWC

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.

alt text

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.

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
task_name data_dir bert_pretrained bert_checkpoint_path
ubuntu data/ubuntu_corpus_v1 bert-base-uncased bert-base-uncased-pytorch_model.bin
douban
e-commerce		 data/douban
data/e-commerce		 bert-base-wwm-chinese bert-base-wwm-chinese_model.bin
BERT-Post
task_name data_dir bert_pretrained bert_checkpoint_path
ubuntu data/ubuntu_corpus_v1 bert-post-uncased bert-post-uncased-pytorch_model.pth
douban data/douban bert-post-douban bert-post-douban-pytorch_model.pth
e-commerce data/e-commerce bert-post-ecommerce bert-post-ecommerce-pytorch_model.pth
ELECTRA-Base
task_name data_dir bert_pretrained bert_checkpoint_path
ubuntu data/ubuntu_corpus_v1 electra-base electra-base-pytorch_model.bin
douban
e-commerce		 data/douban
data/e-commerce		 electra-base-chinese electra-base-chinese-pytorch_model.bin
ELECTRA-Post
task_name data_dir bert_pretrained bert_checkpoint_path
ubuntu data/ubuntu_corpus_v1 electra-post electra-post-pytorch_model.pth
douban data/douban electra-post-douban electra-post-douban-pytorch_model.pth
e-commerce data/e-commerce electra-post-ecommerce electra-post-ecommerce-pytorch_model.pth

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.

Ubuntu [email protected] [email protected] [email protected]
UMS-BERT+ 0.875 0.942 0.988
Douban MAP MRR [email protected] [email protected] [email protected] [email protected]
UMS-BERT+ 0.625 0.664 0.499 0.318 0.482 0.858
E-Commerce [email protected] [email protected] [email protected]
UMS-BERT+ 0.762 0.905 0.986
Owner
Taesun Whang
Interested in NLP, Dialogue System, Multimodal Learning. Currently attending Master's course in Dept. of Computer Science and Engineering, Korea University.
Taesun Whang
GitHub Repository
MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021)

MVFNet: Multi-View Fusion Network for Efficient Video Recognition (AAAI 2021) Overview We release the code of the MVFNet (Multi-View Fusion Network).

2 Jan 29, 2022
[TPAMI 2021] iOD: Incremental Object Detection via Meta-Learning

Incremental Object Detection via Meta-Learning To appear in an upcoming issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T

Joseph K J 66 Jan 04, 2023
ERISHA is a mulitilingual multispeaker expressive speech synthesis framework. It can transfer the expressivity to the speaker's voice for which no expressive speech corpus is available.

ERISHA: Multilingual Multispeaker Expressive Text-to-Speech Library ERISHA is a multilingual multispeaker expressive speech synthesis framework. It ca

Ajinkya Kulkarni 43 Nov 27, 2022
A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generative Modeling" (ICCV 2021)

Manifold Matching via Deep Metric Learning for Generative Modeling A Pytorch implementation of "Manifold Matching via Deep Metric Learning for Generat

69 Dec 10, 2022
交互式标注软件，暂定名 iann

iann 交互式标注软件，暂定名iann。 安装 按照官网介绍安装paddle。 安装其他依赖 pip install -r requirements.txt 运行 git clone https://github.com/PaddleCV-SIG/iann/ cd iann python iann

294 Dec 30, 2022
LETR: Line Segment Detection Using Transformers without Edges

LETR: Line Segment Detection Using Transformers without Edges Introduction This repository contains the official code and pretrained models for Line S

mlpc-ucsd 157 Jan 06, 2023
Dense Gaussian Processes for Few-Shot Segmentation

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxi

37 Jan 07, 2023
Train CNNs for the fruits360 data set in NTOU CS「Machine Vision」class.

CNNs fruits360 Train CNNs for the fruits360 data set in NTOU CS「Machine Vision」class. CNN on a pretrained model Build a CNN on a pretrained model, Res

Ricky Chuang 1 Mar 07, 2022
Deep Learning for Human Part Discovery in Images - Chainer implementation

Deep Learning for Human Part Discovery in Images - Chainer implementation NOTE: This is not official implementation. Original paper is Deep Learning f

Shintaro Shiba 63 Sep 25, 2022
Implementation for Learning to Track with Object Permanence

Learning to Track with Object Permanence A video-based MOT approach capable of tracking through full occlusions: Learning to Track with Object Permane

Toyota Research Institute - Machine Learning 91 Jan 03, 2023
ColossalAI-Benchmark - Performance benchmarking with ColossalAI

Benchmark for Tuning Accuracy and Efficiency Overview The benchmark includes our

HPC-AI Tech 31 Oct 07, 2022
Head and Neck Tumour Segmentation and Prediction of Patient Survival Project

Head-and-Neck-Tumour-Segmentation-and-Prediction-of-Patient-Survival Welcome to the Head and Neck Tumour Segmentation and Prediction of Patient Surviv

5 Oct 20, 2022
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

The Hypersim Dataset For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real i

Apple 1.3k Jan 04, 2023
[CVPR 2021] Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach This is the repo to host the dataset TextSeg and code for TexRNe

SHI Lab 174 Dec 19, 2022
The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection .

GCoNet The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection . Trained model Download final_gconet.pth

Qi Fan 46 Nov 17, 2022
Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

DeepCourse: Deep Learning for Computer Vision arthurdouillard.com/deepcourse/ This is a course I'm giving to the French engineering school EPITA each

Arthur Douillard 113 Nov 29, 2022
This project implements "virtual speed" from heart rate monito

ANT+ Virtual Stride Based Speed and Distance Monitor Overview This project imple

2 May 20, 2022
Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话）. Many modifications t

291 Jan 02, 2023
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

ENet in Caffe Execution times and hardware requirements Network 1024x512 1280x720 Parameters Model size (fp32) ENet 20.4 ms 32.9 ms 0.36 M 1.5 MB SegN

Timo Sämann 561 Jan 04, 2023
WORD: Revisiting Organs Segmentation in the Whole Abdominal Region

WORD: Revisiting Organs Segmentation in the Whole Abdominal Region (Paper and DataSet). [New] Note that all the emails about the download permission o

Healthcare Intelligence Laboratory 71 Dec 22, 2022