iemocap_data.pkl, meld_data_original.pkl and AVEC/* are original datasets in the github
iemocap_data_act.pkl and meld_data_act.pkl are the datasets with dialogue act annotations from https://github.com/bothe/EDAs
meld_data.pkl is the MELD dataset with dialogue act annotations and pretrained 300d visual features for each utterance
Do A Single Run (train/valid/test)
Set up the configurations in config/run.ini
python run.py -config config/run.ini
Configuration setup
Dialogue
mode = run
pickle_dir_path = /path/to/datasets/. The absolute path of the folder storing the datasets.
wordvec_path = /path/to/glove_embedding/. The path of the glove embedding file.
dataset_name in {'iemocap','meld','avec'}. Name of the dataset.
features in {'acoustic','visual','textual'}. Multiple modality names should be joined by ','.
label = emotion. Only emotion recognition is supported for the time being.
dialogue_context in {'True','False'}. Whether the data is converted to context + current utterance format (the context of a certain length is extracted for each utterance in the dialogue).
context_len. Length of the context. Requires dialogue_context = True.
seed. The random seed for the experiment.
load_model_from_dir in {'True','False'}. Whether the model is loaded from a saved file.
dir_name. The directory storing the model configurations and model parameters. Requires load_model_from_dir = True.
fine_tune in `{'True','False'}. Whether you want to train the model with the data.
model specific parameters. For running a model on the dataset, uncomment the respective area of the model and comment the areas for the other models. Please refer to the model implementations in /models/dialogue/ for the meaning of each model specific parameter.
supported models include but are not limited to:
BC-LSTM
CMN
ICON
DialogueRNN
DialogueGCN
Multimodal-Transformer
EF-LSTM
LF-LSTM
TFN
MFN
MARN
RMFN
LMF
Vanilla-LSTHM
QMN (QMNN)
Each model is only compatible with dialogue_context = True or dialogue_context = False. For this information, please see the respective block for each model.
Grid Search for the Best Parameters
Set up the configurations in config/grid_search.ini. Tweak a couple of fields in the single run configurations, as instructed below.
Write up the hyperparameter pool in config/grid_parameters/.
python run.py -config config/grid_search.ini
Configuration setup
mode = run_grid_search
grid_parameters_file. The name of file storing the parameters to be searched, under the folder /config/grid_parameters.
the format of a file is:
[COMMON]
var_1 = val_1;val_2;val_3
var_2 = val_1;val_2;val_3
search_times. The number of times the program searches in the pool of parameters.
output_file. The file storing the performances for each search in the pool of parameters. By default, it is eval/grid_search_{dataset_name}_{network_type}.csv
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