Comments (4)
Thanks for your interest!
inter_dist is computed regarding each batch (batch size=1). We want to evaluate how many distinct n-grams the model generates for each context rather than all contexts. #n_samples is always greater than 1 in our evaluation.
We averaged the results for all batches in the test set.
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Thanks for your reply!
Since DialogWAE samples multiple responses for each context, the inter_dist makes its sense.
However, for other deterministic models, i.e., HRED, only one response is generated for the given context.
Thus, does the inter_dist metric for those models computed regarding all contexts?
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For deterministic models, we repeated each generated response for n_samples times.
I think it is equivalent to compute regarding all contexts.
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Thanks for your patient explanations!
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Related Issues (11)
- Stop training the context during train_G/train_D? HOT 3
- Code explanation HOT 3
- SWDA seems to give lot of repetition in the sample responses for the test data. HOT 4
- Unable to achieve published result in DailyDialogue HOT 3
- Wasserstein distance between prior and posterior HOT 3
- Warning when run sample.py:RNN module weights are not part of single contiguous chunk of memory. HOT 3
- seems the loss of both generator and discriminator would collapse? HOT 1
- Could I apply for your pretrained model in DailyDialog?
- It is reasonable if I set n_samples to 1 when I run the sample.py HOT 2
- Code explanation about data prepocessing HOT 2
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