Relation Discovery with Out-of-Relation Knowledge Base as Supervision
Table 3: Comparison results on NYT122 with different prediction and regularization strategies (using encoder or decoder).
['[BOLD] Model', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on encoder', '[BOLD] Metrics Prediction based on decoder', '[BOLD] Metrics Prediction based on decoder', '[BOLD] Metrics Prediction based on decoder', '[BOLD] Metrics Prediction based on decoder']
From the table, we can see that regularization with Euclidean distance performs the best compared to KL and JS. Moreover, the regularization over encoder is better than the regularization over decoder. This may be because the way that we put constraints only over sampled sentences in a batch may hurt the regularization of decoder, since sampled unique pairs may be less than sample sentences. If we look at results comparing original DVAE prediction based on the encoder and the decoder, both result in similar F1 and NMI numbers. Thus, we can only conclude that currently in the way we do sampling, constraining over encoder is a better choice.