Towards UniversalParaphrastic Sentence Embeddings
Table 6: Results from supervised training on similarity, entailment, and sentiment tasks, except that we keep the sentence representations fixed to our paragram-phrase model. The first row shows Pearson’s r×100 and the last two show accuracy, with boldface showing the highest score in each row.
['Task', 'paragram-phrase 300', 'paragram-phrase 1200', 'paragram-phrase 2400', 'skip-thought uni-skip', 'skip-thought bi-skip']
The similarity and entailment tasks show clear improvements as we project the embeddings into the 2400 dimensional space. In fact, our results outperform both types of skip-thought embeddings on the single task that we overlap. However, the sentiment task does not benefit from higher dimensional representations, which is consistent with our regularization experiments in which sentiment also did not show improvement. Therefore, it seems that our models learned from PPDB are more effective for similarity tasks than classification tasks, but this hypothesis requires further investigation.