DialogueGLP: Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation
This is code for "Global-Local Modeling with Prompt-Based Knowledge Enhancement for Emotion Inference in Conversation".
torch = 1.10.1
torch_geometric = 2.0.4
transformers = 4.17.0
For most baselines and DialogueGLP, first train a feature extractor which is trained directly with (utterance, emotion) pairs. Take DailyDialog dataset as an example, the following trains a roberta-large feature extractor on DailyDialog.
# DailyDialog weighted
python feature_tuning.py \
--name feature_extractor_dd_weighted \
--model Extractor \
--dataset daily_dialogue \
--batch_size 16 \
--lr 5e-6 \
--epochs 4 \
--seed 42 \
--metric weightedAfter training with (utterance, emotion) pairs, we use the trained model from previous step to extractor features.
python feature_extraction.py \ --name feature_extractor_dd_weighted \ --model Extractor \ --dataset daily_dialogue \ --metric weighted
After extracting the features, we can now train DialogueGLP. The following gives a shell script example to train DialogueGLP on DailyDialog with 5 random seeds. You can also find other scripts to train other models in scripts folder.
# you can also run scripts/base_model_dd_weighted.sh metric="weighted" knowledge="none" for seed in 42 0 1 2 3 do cmd="python train.py \ --name base_${metric}_$seed \ --model BaseModel \ --dataset daily_dialogue \ --batch_size 8 \ --gradient_accumulation_steps 4 \ --seed $seed \ --metric $metric \ --scheduler cosine \ --lr 0.001 \ --plm_lr 1e-5 \ --feature_metric weighted \ --epochs 16 \ --knowledge $knowledge" echo "${cmd}" eval $cmd done
You can also add a new model and test its performance of inferring emotions. Basically you should follow the following steps:
- Add the model file to model folder and write a wrapper for the model in model/base_models.py
- Add dataset and collator for that model in data/dataset.py
- Add arguments in utils/options.py
DialogueGLP
├── data
│ ├── daily_dialogue
│ │ ├── knowledge
│ │ ├── test.csv
│ │ ├── train.csv
│ │ └── validation.csv
│ ├── emorynlp
│ │ ├── knowledge
│ │ ├── ...
│ │
│ ├── meld
│ │ ├── knowledge
│ │ ├── ...
│ │
│ ├── dataset.py
│ ├── load_data.py
│ └── process_data.py
├── model
│ ├── cog_bart
│ ├── com_pm
│ ├── dag_erc
│ ├── dialogue_gcn
│ ├── base_models.py
│ ├── dialogue_crn.py
│ ├── dialogue_infer.py
│ ├── dialogue_rnn.py
│ └── ei_roberta.py
├── scripts
│ ├── base_model_dd_weighted.sh
│ ├── ...
│
├── utils
│ ├── options.py
│ ├── utils.py
│ ├── ...
│
├── feature_extraction.py
├── feature_tuning.py
└── train.py