Don’t Settle for Average, Go for the Max: Fuzzy Sets and Max-Pooled Word Vectors
Table 5: Mean Pearson correlation on STS tasks for the ablation studies. As described in Appendix C, it is clear that the three components of the algorithm — the dynamic universe, the max-pooling operation, and the fuzzy Jaccard index — all contribute to the strong performance of DynaMax-Jaccard.
['[BOLD] Ablation on', '[BOLD] Approach', '[BOLD] STS12', '[BOLD] STS13', '[BOLD] STS14', '[BOLD] STS15', '[BOLD] STS16']
First, we show that the dynamic universe is superior to other sensible choices, such as the identity and random 300×300 projection with components drawn from N(0,1). Next, we show that the fuzzy Jaccard index beats the standard cosine similarity on 4 out 5 benchmarks. Finally, we find that max considerably outperforms other pooling operations such as averaging, sum and min. We conclude that all three components of DynaMax are very important. It is clear that max-pooling is the top contributing factor, followed by the dynamic universe and the fuzzy Jaccard index, whose contributions are roughly equal.