Combining In-Domain and Auxiliary Examples During Training
True or False: A training set may include a smaller set of target-domain examples together with a larger auxiliary dataset so the model learns from both.
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Combining In-Domain and Auxiliary Examples During Training
Adding target-distribution examples to training means the training set now includes data from the _____ distribution.
Match the training-set components with their distribution role in a model built for a specific target domain.
Order the steps for allocating product-review data between target and auxiliary sources.
Why can it help to include some target-distribution audio in both the training set and the training-dev set?
True or False: It can still be worthwhile to train on a dataset that comes from a different distribution than the dev/test set.
Strong results on source examples but weak results on deployment examples validate the hypothesis that more _____ data would help.
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Why Include a Small Amount of Target-Distribution Data in Training?
Using Target-Distribution Samples to Guide Training Decisions
Checking Whether More Same-Distribution Data Would Help