Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Table 4: Performance comparisons on Audio set.
['Models Baselines', 'Models Baselines', 'maF1 Baselines', 'miF1 Baselines', 'ebF1 Baselines', 'ACC Baselines', 'HA Baselines', 'Average Baselines']
In the following, we show results of the baseline models and the proposed method on three text datasets. For MTL models, we show the results of the four kinds of different decoding strategies described in section Decoder Integration. For a simple comparison, we also compute averages of the five metrics as a reference. Note that blue an bold texts in Table In this experiment, all models have similar performance in HA. Surprisingly, BR is a competitive baseline model and performs especially well in miF1. Seq2seq achieves the best performance in terms of ACC, which is the same as the observation on AAPD. Overall, OCD performs better than all the baseline and MTL indeed improves the performance. OCD outperforms other RNN decoder-based models in maF1, miF1 and ebF1 and performs worse than BR only in terms of miF1.