Simple and Effective Paraphrastic Similarity from Parallel Translations
Table 1: Comparison between training on 1 million examples from a backtranslated English-English corpus (en-en) and the original bitext corpus (en-cs) sampling 1 million and 2 million sentence pairs (the latter equalizes the amount of English text with the en-en setting). Performance is the average Pearson’s r over the 2012-2016 STS datasets.
['Model', 'en-en', 'en-cs(1M)', 'en-cs(2M)']
First, models trained on en-en, in contrast to those trained on en-cs, have higher correlation for all encoders except SP. However, when the same number of English sentences is used, models trained on bitext have greater than or equal performance across all encoders. Second, SP has the best overall performance in the en-cs setting. It also has fewer parameters and is faster to train than BLSTM-SP and Trigram. Further, it is faster at encoding new sentences at test time.