GLOSS: Generative Latent Optimization of Sentence Representations
Table 3: Effect of dimensionlity: additional results for the test performance of GLOSS-BoW and GLOSS-Pos models with different dimensionality of the latent vectors on unsupervised STS-(12-16, B) and supervised tasks. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. Best results for each task are in bold.
['Model', 'Config. #Tok', 'Config. Dim', 'Unsupervised STS-() tasks 12', 'Unsupervised STS-() tasks 13*', 'Unsupervised STS-() tasks 14', 'Unsupervised STS-() tasks 15', 'Unsupervised STS-() tasks 16', 'Unsupervised STS-() tasks B', 'Unsupervised STS-() tasks Avg', 'Supervised tasks MR', 'Supervised tasks CR', 'Supervised tasks SUBJ', 'Supervised tasks MPQA', 'Supervised tasks TREC', 'Supervised tasks Avg']
Test performance on unsupervised STS-(12-16, B) and supervised tasks. (α) indicate results computed by us. (β) PV-DBOW results are taken from Pagliardini et al. (†) The unsupervised results for Skip-thought are taken from Arora et al. (∗) Following SentEval, STS-13 does not include the SMT dataset due to licensing issues. The STS-B scores of other baselines are from the official webpage, except for InferSent which are from Wieting and Gimpel All other results are from the respective publications. We observe that GLOSS-BoW is better than GLOSS-POS for unsupervised tasks (cf. Generally, increasing dimensionality does not improve accuracy on unsupervised tasks. This is in line with Hill et al.