Content Comparison

...

1. Write a Distribution Analysis Function

   • See mainrun_train_test.py for the incomplete function.

   • This function should calculate how often each intent and slot appears in the dataset.

   • Think about what fields in the dataset you’ll need:

     • Intent: Directly accessible as example['intent'].

     • Slots: Comes from example['slots'] but might need to be flattened into a single list.

   • Use a counting method to track the frequency of intents and slots.

   Tips:

   • Use tools like collections.Counter for efficient counting.

   • Ensure your function handles edge cases, such as examples without any slots.

2. Run the Function on Training and Testing Data

   • Call your distribution function for both datasets.

   • Print the results to inspect the frequency of each intent and slot.

   Tips:

   • Compare the distributions of training and test datasets.

   • Look for imbalances or unexpected gaps. For example:

     • Are certain intents or slots underrepresented or missing?

     • Does the test set mirror the training set?

3. Interpret the Results

   • Once you have the distributions, analyze them to answer key questions:

     • Which intents or slots are the most frequent? The least?

     • Are there any imbalances that might cause the model to focus too heavily on common labels?

     • Are rare intents or slots important for the system’s performance?

   • Reflect on how these observations might affect training.

   Tips:

   • If rare intents or slots are crucial, consider strategies like data augmentation or using weighted loss functions during training.

   • If the test distribution doesn’t match the training distribution, think about how this might affect evaluation.

...