STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Table 2. Comparision of STEAM against the baseline methods on the three datasets (in %). To reduce the randomness, we ran all methods for three times and report the average performance. TAXI outputs an entire taxonomy instead of ranking lists, so we are unable to obtain the MRRs for it.
['Dataset Metric', '[BOLD] Environment Acc', '[BOLD] Environment MRR', '[BOLD] Environment Wu&P', '[BOLD] Science Acc', '[BOLD] Science MRR', '[BOLD] Science Wu&P', '[BOLD] Food Acc', '[BOLD] Food MRR', '[BOLD] Food Wu&P']
From the results, we have the following observations: The Effect of Co-training. Now we proceed to study the effectiveness of the co-training procedure. While integrating multiple views is important, how to integrate multi-view information is equally important. This verifies the effectiveness of co-training compared with concatenation : the simple concatenation strategy cannot fully harvest the information from each view and could make the learning problem more difficult. Interestingly, the performance for Concat is even worse than Concat-D and Concat-C in accuracy on Food and Environment, which implies that simple concatenation can even hurt the performance with more views. The co-training objective in STEAM involves two loss terms that encourage better learning of the base classifiers and the consistency among them. STEAM-Co only uses the aggregated classifier for prediction and underperforms STEAM by large margins. The reason is that these terms explicitly require every base classifier is sufficiently trained and mutually enhances each other; without them, certain views may not be fully leveraged, which limit the effectiveness in leveraging multi-view information for training.