Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings
Table 1: Test results on SemEval semantic textual similarity datasets (Pearson’s r×100) when training on different sources of data: phrase pairs from PPDB or simple-to-standard English Wikipedia sentence pairs from coster2011simple.
['[EMPTY]', 'avg', 'LSTM', 'LSTMavg']
We first note that, when training on PPDB, we find the same result as \newcitewieting-16-full: avg outperforms the LSTM by more than 13 points. However, when training both on sentence pairs, the gap shrinks to about 9 points. It appears that part of the inferior performance for the LSTM in prior work was due to training on phrase pairs rather than on sentence pairs. The avg model also benefits from training on sentences, but not nearly as much as the LSTM. When training on PPDB, moving from LSTM to LSTMavg improves performance by 10 points, closing most of the gap with avg. We also find that LSTMavg improves by moving from PPDB to SimpWiki, though in both cases it still lags behind avg.