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Neural Conversation Models

Open-sources of neural conversation models

Environment

We've tested the code in this environment

  • Python 3.7.3
  • PyTorch 1.1.0

Implemented conversation models

How to run the code

Check the 'NCM' folder. Please see the slides in XAI Workshop in slides folder.

Reference

Speaker Sensitive Response Evaluation Model

Overview

This repo provides the implementation of Speaker Sensitive Response Evaluation Model (SSREM).

Tested Environmnet

  • Python 3.6.3
  • Pytorch 1.3

How to run

In 'SSREM' folder, we make bash script file to train and evaluate SSREM. All arguments for the bash files are passed into argparse in configs.py/

  • Run_train.sh: a bash script file to train SSREM

bash Run_train.sh 0 TC SSREM 5000 2000 1e-4

  • Run_eval1.sh: a bash script file to identify the true and false responses

bash Run_eval1.sh 0 TC SSREM 5000 ../results/TC/SSREM/20191209_235959/2000.pkl 4

Data

We use Twitter conversation corpus: https://www.aclweb.org/anthology/D19-1202/ Please contact to the authors of the paper to get the corpus.

Discussion

Please let us know if you have any question or requests by issues or email. (First author of the paper page: https://nosy.github.io/)

Reference



XAI Project

This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence)

  • Project Name : A machine learning and statistical inference framework for explainable artificial intelligence (의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)

  • Managed by Ministry of Science and ICT/XAIC

  • Participated Affiliation : KAIST, Korea Univ., Yonsei Univ., UNIST, AITRICS

  • Web Site : http://openXai.org