ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Table 10: The means (over all 25 STS competition datasets) of the absolute differences in Pearson’s r between each pair of models.
['Models', 'Mean Pearson Abs. Diff.']
Overall, our individual models (Word, Trigram, LSTM) perform similarly. Using 300 dimensions appears to be sufficient; increasing dimensionality does not necessarily improve correlation. When examining particular STS tasks, we found that our individual models showed marked differences on certain tasks. ’s r over all 25 datasets. The Trigram model shows the largest differences from the other two, both of which use word embeddings. This suggests that Trigram may be able to complement the other two by providing information about words that are unknown to models that rely on word embeddings.