ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Table 14: General-purpose sentence embedding tasks, divided into categories based on resource requirements.
['[BOLD] Model', '[BOLD] Dim.', '[BOLD] MR', '[BOLD] CR', '[BOLD] SUBJ', '[BOLD] MPQA', '[BOLD] SST', '[BOLD] TREC', '[BOLD] MRPC', '[BOLD] SICK-R', '[BOLD] SICK-E']
Each of our individual models produces 300-dimensional sentence embeddings, which is far fewer than the several thousands (often 2400-4800) of dimensions used in most prior work. While using higher dimensionality does not improve correlation on the STS tasks, it does help on the general sentence embedding tasks. Using higher dimensionality leads to more trainable parameters in the subsequent classifiers, increasing their ability to linearly separate the data.