An efficient framework for learning sentence representations
Table 9: Training time and performance for different embedding sizes. The reported performance is the mean accuracy over the classification benchmarks (MSRP, TREC, MR, CR, SUBJ, MPQA).
['Embedding size', 'Training time (h)', 'Performance']
The training times listed here assume that the two component models in MC-QT are trained in parallel. The reported performance is an average over all the classification benchmarks (MSRP, TREC, MR, CR, SUBJ, MPQA). We note that the classifiers trained on top of the embeddings for downstream tasks differ in size for each embedding size. So it is difficult to make any strong conclusions about the quality of embeddings for the different sizes. However, we are able to reduce the embedding size and train the models more efficiently, at the expense of marginal loss in performance in most cases.