Decoding Decoders: Finding Optimal Representation Spaces For Unsupervised Similarity Tasks
Table 5: Performance of different architectures and sentence representations on unsupervised similarity tasks using dot product as the similarity measure. On each task, the highest performing setup for each encoder type is highlighted in bold and the highest performing setup overall is underlined. All reported values indicate Pearson/Spearman correlation coefficients for the task.
['[BOLD] Encoder', '[BOLD] Decoder', '[BOLD] STS12', '[BOLD] STS13', '[BOLD] STS14', '[BOLD] STS15', '[BOLD] STS16']
While the strong performance of \glsrnn-\glsrnn-mean relative to \glsrnn-\glsrnn is consistent with our theory, exploring why it is able to outperform \glsrnn-concat experimentally on \glssts tasks is left to future work.