Machine Learning @EPFL 2023

Code for Commonsense Persona-Grounded Dialogue Challenge (CPDC 2023).

Adjusted from LMEDR official implementation

Team @Zewei Zhang, @Yihan Wang

Requirements

Check the package requirements

• python==3.8
• torch==1.9.1
• transformers==4.14.1
• pytorch-ignite==0.4.9

Please install ParlAI, which can be done in the following ways

git clone https://github.com/Chenrj233/ParlAI.git
cd ParlAI
python setup.py install # you can try <pip install -e . --no-cache-dir> instead in case of failure

Please replace eval_f1.py and eval_hits.py in /ParlAI/projects/convai2/ with the corresponding files in /other/. Similarly, replace the generation_utils.py in transformers/ with the corresponding files in /other/, the file is in a path similar to

| -- python3.8
	| -- site-packages
		| -- transformers
			| -- modeling_utils.py
			| -- generation_utils.py
			| -- ...

Data

The datasets processed and used in this project can be obtained from the following link:

Dataset	Used in
PersonaChat-PEACOK	Training the DDM
DNLI	Training the ERM

PersonaChat-PEACOK contains all four datasets used in our report. The details of the models and used datasets are given as follows:

Model	Dataset Used	Epoch Number
Our best model	Original_best	10
Case 1 (Table V)	Induced_retrieved_2_1	1
Case 2 (Table V)	Induced_retrieved_kmax5	1
Case 3 (Table V)	Induced_retrieved_10_2	1

Please directly download the files in folder Dataset/LMEDR for the LMEDR model training.

Training

Put the downloaded datasets in corresponding subdirs in data/, change the datapaths in line #135 & #138 in train_PersonaChat.py and train_PersonaChat_partner.py, use the following script to train without partner persona constraints. If you want to train on the revised dataset, please add --revised. We also provide a launch file other/chat_train.bat that can be used on EPFL HPC resource Scitas.

python train_PersonaChat.py --lr 8e-6 \
--epochs 20 \
--train_batch_size 2 \
--valid_batch_size 2 \
--infer_batch_size 64 

Use the following script to train with partner persona constraints:

python train_PersonaChat_partner.py --lr 8e-6 \
--epochs 20 \
--train_batch_size 2 \
--valid_batch_size 2 \
--infer_batch_size 64 

Evaluation

Our current best-performing model checkpoints can be obtained from here, place it under persona_original.

• Hits@1

python evaluation_PersonaChat.py --model_checkpoint persona_original/Model/Original_best_model \
--eval_type hits@1

• F1

python evaluation_PersonaChat.py --model_checkpoint persona_original/Model/Original_best_model \
--eval_type f1 \
--beam 2 \
--max_history 7

• PPL

python train_PersonaChat.py --load_from persona_original/Model/Original_best_model \
--eval

• C.Score

  Please refer to PAML.

Generate dialogue prediction

Please adjust the checkpoint path in Infer_Generation/agents/LMEDR_agents.py and run the following code to generate dialogue. We provide dummy input context in Infer_Generation/dummy_data.json, you can follow the structure and create your own input data.

cd Infer_Generation
python local_evaluation.py

Check out our current best model's response: