Models
This section describes models used for comparative study.
Last updated
This section describes models used for comparative study.
Last updated
Describe existing models or frameworks commonly adopted by your models (if any):
All our models adopt MODEL (CITATION) as the encoder.
List all models used for your experiments. Give a brief description of each model by referencing specific sections explaining the core methods used by the model:
The following three models are experimented:
BASELINE: DESCRIPTION (Section #)
ADVANCED: BASELINE + METHOD (Section #)
BEST: ADVANCED + METHOD (Section #)
Because a neural model produces a different result every time trained, you need to train it 3 ~ 5 times and report its average score with the standard deviation ().
Why would a neural model produce a different result every time it is trained?
Thus, indicate how many times each model is trained and what is used as the evaluation metric(s):
Every model is trained 3 times and its average F1-score and the standard deviation is used as the evaluation metric.
If you use a non-standard metric that has not been used in previous work because:
The task is new,
The new aspect introduced for this task has never been tested before,
You find a better way of evaluating this, which has not been used in the previous work
explain why you cannot apply standard metrics to evaluate this task and describe the new metric:
Since TASK has not been evaluated on ASPECT(S) in prevoius work, we introduce new metrics ...
Describe hyper-parameters used to build the models (e.g., epoch, learning rate, hidden layer, optimizer, batch size):
MODEL is trained for # epochs using the learning rate of FLOAT, ...
Explain anything special that you do for training:
Early stop is adopted to control the number of epochs if the score on the development set does not improve over two epochs.
Describe computing devices used for the experiments:
Our experiments use NVIDIA Titan RTX GPUs, which takes 10/20/30 hours for training the BASELINE/ADVANCED/BEST MODELS, respectively.
If you observe enhanced training efficiency (e.g., your new loss function requires a fewer number of epochs to train), create a figure (e.g., x-axis: epochs, y-axis: accuracy) describing the training processes of the baseline and the enhanced models.
Our ENHANCED MODEL reaches the same accuracy (or higher) than the BASELINE model after only a third of epochs.
If you experience unusual phenomena during training (e.g., results on the development set are unstable), describe the phenomena and analyze why they are happening: