Towards UniversalParaphrastic Sentence Embeddings
Table 4: Results on SemEval textual similarity datasets (Pearson’s r×100) for paragram-phrase XXL embeddings. Results that match or exceed the best shared task system are shown in bold. ∗For the 2014 SICK task, the median, 75th percentile, and maximum include only the primary runs as the full set of results was not available.
['Dataset', '50%', '75%', 'Max', 'paragram-phrase-XXL']
Since we found that paragram-phrase embeddings have such strong performance, we trained this model on more data from PPDB and also used more data for hyperparameter tuning. For tuning, we used all of PPDB XL and trained for 10 epochs, then trained our final model for 10 epochs on the entire phrase section of PPDB XXL, consisting of 9,123,575 unique phrase pairs. We also report the median, 75th percentile, and maximum score from our suite of textual similarity tasks. paragram-phrase XXL matches or exceeds the best performance on 4 of the datasets (SMT-news, SMT, deft forum, and belief) and is within 3 points of the best performance on 8 out of 22. We have made this trained model available to the research community.