5.2. Models
This section describes models used for comparative study.
Descriptions
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 #)
It is important to design models in a way that clearly shows key differences in methods.
Evaluation Metrics
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 (Section 5.3).
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 experiment on datasets used in previous work, you must use the same evaluation metrics for fair comparisons. Even if you present a new metric, you still need to evaluate both the old and new metrics to show the advantage of your new 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 ...
If your new evaluation metric is novel that requires bigger attention, explain it in the approach section so it would be considered one of the main contributions.
Experimental Settings
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.
It is important to describe the experimental settings for replication, although they are often put in the appendix due to the page limit for the actual paper submission.
Development
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:
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