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Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge

Source codes for the baseline models of Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge, accepted at AAAI-22.

Environment Setting

We trained the models under the setting of python==3.7 and torch==1.5.0, with one RTX8000 GPU. Also, our codes are built on the codes of huggingface, and we utilized pytorch-ignite from pytorch in ignite folder.

1.Make a virtual environment

$conda create -n ENV_NAME python=3.7

2.Install pytorch==1.5.0

$conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.2 -c pytorch

3.Install the required libraries.

$pip install -r requirements.txt

Dataset [FoCus dataset v2]

This data is the modified version of the original data (which is reported in the paper) after ethical inspection.

FoCus v2 STATISTICS	Train	Valid
`# dialogues`	12,484	1,000
`# avg rounds`	5.63	5.64
`# knowledge-only answers`	37,488	3,007
`# persona-knowledge answers`	32,855	2,630
`# landmarks`	5,152	923
`avg len of Human's utterances`	40.70	40.21
`avg len of Machine's utterances`	138.16	138.60

You should create directories named infer_log_focus, train_log_focus, test_log_focus, models, data under FoCus folder.

We put train, valid, test files of the dataset in the data folder. (The test set will be available after March 2022.)

The project directory should follow this directory structure:

📦FoCus
┣ 📂data
┃ ┗ 📜train.json
┃ ┗ 📜valid.json
┣ 📂ignite
┣ 📂infer_log_focus
┣ 📂models
┣ 📂python_tf_idf
┣ 📂test_log_focus
┣ 📂train_log_focus
┣ 📜classification_modules.py
┣ 📜data_utils.py
┣ 📜evaluate_test.py
┣ 📜evaluate_test_ppl.py
┣ 📜inference.sh
┣ 📜inference_test.py
┣ 📜LICENSE
┣ 📜README.md
┣ 📜requirements.txt
┣ 📜test.sh
┣ 📜train.sh
┣ 📜train_focus.py
┗ 📜utils_focus

Training the models

Uncomment the command lines in the train.sh file, to start training the model.

$ sh train.sh

Evaluation

Uncomment the command lines in the test.sh file, to evaluate the model on the test set.

$ sh test.sh

Inference

Uncomment the command lines in the inference.sh file, to generate utterances with the trained models.

$ sh inference.sh

Official Test Set

You can evaluate your model on the official test set here.

Our Workshop @ COLING 2022

We held the 1st workshop on Customized Chat Grounding Persona and Knowledge at COLING 2022.

Citation

To use our data or source code, please cite our paper:

@inproceedings{jang2022call,
  title={Call for Customized Conversation: Customized Conversation Grounding Persona and Knowledge},
  author={Jang, Yoonna and Lim, Jungwoo and Hur, Yuna and Oh, Dongsuk and Son, Suhyune and Lee, Yeonsoo and Shin, Donghoon and Kim, Seungryong and Lim, Heuiseok},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={10},
  pages={10803--10812},
  year={2022}
}

Written by Yoonna Jang.