P-SIF: Document Embeddings Using Partition Averaging
Table 8: Experimental results (Pearson’s r × 100) on textual similarity tasks on STS 16. The highest score in each row is in bold.
['[BOLD] Tasks  [BOLD] thoughts', '[BOLD] Skip  [BOLD] LSTM', '[BOLD] Tree LSTM', '[BOLD] Sent2Vec', '[BOLD] Doc2Vec', '[BOLD] GloVe', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]', '[EMPTY]']
In this supplementary section, we present the details of the STS tasks for each year. Note that tasks with the same name in different years are different tasks in reality. Our method performs significantly better in comparison to all unsupervised embedding methods. In addition, P-SIF is very close to the best performance by supervised methods on the rest of the datasets. Our method was also able to outperform state of the art supervised averaging based Gated Recurrent Averaging Network (GRAN) Our results also outperform state of the art methods on many recent supervised embedding methods on the STS 16 task (