Smart Tags
When Azimuth is launched, smart tags are computed automatically (or loaded from the cache) on all utterances, on both training and evaluation sets.
Conceptually, smart tags are meta-data on the utterance and/or its prediction. They help to narrow down data samples to identify those that may require further action and investigation. Different families of smart tags exist, based on the different types of analyses that Azimuth provides.
The current list of supported smart tag, and their families, is detailed below.
Extreme Length
Syntax Analysis gives more details on how the syntactic information is computed.
multiple_sentences: The number of sentences is above 1. All other syntactic smart tags will be disabled when this is the case.long_sentence: The number of tokens is above 15.short_sentence: The number of tokens is less than or equal to 3.
Partial Syntax
Syntax Analysis gives more details on how the syntactic information is computed.
missing_subj: The sentence is missing a subject.missing_verb: The sentence is missing a verb.missing_obj: The sentence is missing an object.
Dissimilar
Similarity Analysis provides more information on how similarity is computed.
conflicting_neighbors_train: The utterance has very few (or no) neighbors from the same class in the training set.conflicting_neighbors_eval: The utterance has very few (or no) neighbors from the same class in the evaluation set.no_close_train: The closest utterance in the training set has a cosine similarity below a threshold (default = 0.5).no_close_eval: The closest utterance in the evaluation set has a cosine similarity below a threshold (default = 0.5).
Almost Correct
These smart tags do not come from a particular analysis. They are computed based on the predictions and the labels.
correct_top_3: The top 1 prediction is not the right one, but the right one is in the top 3.correct_low_conf: The top 1 prediction was the right one, but its confidence is below the threshold, and thus the rejection class was predicted.
Behavioral Testing
Behavioral Testing lists all the tests that are executed.
failed_fuzzy_matching: At least one fuzzy matching test failed.failed_punctuation: At least one punctuation test failed.
Pipeline Comparison
Smart tags that are computed based on the difference between pipelines predictions.
incorrect_for_all_pipelines: When all pipelines give the wrong prediction.pipeline_disagreement: When at least one of the pipelines disagrees with the others.
Uncertain
Uncertainty Quantification provides more details on how the uncertainty is estimated.
high_epistemic_uncertainty: If an uncertainty config was defined, this tag will highlight predictions with high epistemic uncertainty.