Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
Table 6: F1-scores and coverage scores for each train-test pair for the slot-filling experiment. Training on the unique data produces a more robust model, with consistently high performance across test sets.
['Metric', 'Training', 'Test Set same', 'Test Set random', 'Test Set unique']
Again, we see the same trends, with training on same or random leading to low results on the unique dataset, but not the reverse, and similarly for coverage, though the gaps are smaller than for classification.