MuSe-2024 Baseline Model: GRU Regressor

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Sub-challenges and Results

For details, please see the Baseline Paper. If you want to sign up for the challenge, please fill out the form here.

• MuSe-Perception: predicting 16 different dimensions of social perception (e.g. Assertiveness, Likability, Warmth,...). Official baseline: .3573 mean Pearson's correlation over all 16 classes.

• MuSe-Humor: predicting the presence/absence of humor in cross-cultural (German/English) football press conference recordings. Official baseline: .8682 AUC.

Installation

It is highly recommended to run everything in a Python virtual environment. Please make sure to install the packages listed in requirements.txt and adjust the paths in config.py (especially BASE_PATH and HUMOR_PATH and/or PERCEPTION_PATH, respectively).

You can then, e.g., run the unimodal baseline reproduction calls in the *_full.sh file provided for each sub-challenge.

Settings

The main.py script is used for training and evaluating models. Most important options:

• --task: choose either perception or humor
• --feature: choose a feature set provided in the data (in the PATH_TO_FEATURES defined in config.py). Adding --normalize ensures normalization of features (recommended for eGeMAPS features).
• Options defining the model architecture: d_rnn, rnn_n_layers, rnn_bi, d_fc_out
• Options for the training process: --epochs, --lr, --seed, --n_seeds, --early_stopping_patience, --reduce_lr_patience, --rnn_dropout, --linear_dropout
• In order to use a GPU, please add the flag --use_gpu
• Predict labels for the test set: --predict
• Specific parameter for MuSe-Perception: label_dim (one of the 16 labels, cf. config.py), win_len and hop_len for segmentation.

For more details, please see the parse_args() method in main.py.

Reproducing the baselines

Please note that exact reproducibility can not be expected due to dependence on hardware.

Unimodal models

For every challenge, a *_full.sh file is provided with the respective call (and, thus, configuration) for each of the precomputed features. Moreover, you can directly load one of the checkpoints corresponding to the results in the baseline paper. Note that the checkpoints are only available to registered participants.

A checkpoint model can be loaded and evaluated as follows:

main.py --task humor --feature faus --eval_model /your/checkpoint/directory/humor_faus/model_102.pth

Late Fusion

We utilize a simple late fusion approach, which averages different models' predictions. First, predictions for development and test set have to be created using the --predict option in main.py. This will create prediction folders under the folder specified as the prediction directory in config.py.

Then, late_fusion.py merges these predictions:

• --task: choose either humor or perception
• --label_dim: for MuSe-Perception, cf. PERCEPTION_LABELS in config.py
• --model_ids: list of model IDs, whose predictions are to be merged. These predictions must first be created (--predict in main.py or personalisation.py). The model id is a folder under the {config.PREDICTION_DIR}/humor for humor and {config.PREDICTION_DIR}/perception/{label_dim} for perception. It is the parent folder of the folders named after the seeds (e.g. 101). These contain the files predictions_devel.csv and predictoins_test.csv
• --seeds: seeds for the respective model IDs.

Model Checkpoints

Checkpoints for the Perception Sub-Challenge

Checkpoints for the Humor Sub-Challenge

Citation:

The MuSe2024 baseline paper is only available in a preliminary version as of now: https://www.researchgate.net/publication/380664467_The_MuSe_2024_Multimodal_Sentiment_Analysis_Challenge_Social_Perception_and_Humor_Recognition