Learning Paraphrastic Sentence Embeddingsfrom Back-Translated Bitext
Table 7: Quality filtering test results after tuning quality hyperparameters on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).
['Filtering Method', 'GRAN', 'Avg']
The translation cost and language model are not helpful for filtering, as random selection outperforms them. Both methods are outperformed by the reference classifier, which slightly outperforms random selection when using the stronger GRAN model. We now discuss further how we trained the reference classifier and the data characteristics that it reveals. We did not experiment with quality filtering for SimpWiki since it is human-written text.