Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Table 2: Performance on AAPD
['Models (a) Seq2set (simp.)', 'Models (a) Seq2set (simp.)', 'maF1', 'miF1', 'ebF1', 'ACC', 'HA', 'Average']
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 We see that different models are skilled at different metrics. For example, RNN decoder based models, i.e. Seq2seq in row (f) and Order-free RNN in row (h), perform well on ACC, whereas BR and BR++ have better results in terms of HA but show clear weaknesses in predicting rare labels (cf. especially maF1). However, OCD in row (j) performs better than all the baselines (row (d)–(i))