Learning Paraphrastic Sentence Embeddingsfrom Back-Translated Bitext
Table 5: Test correlations for our models when trained on sentences with particular length ranges (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).
['Data', 'Model', 'Length Range 0-10', 'Length Range 10-20', 'Length Range 20-30', 'Length Range 30-100']
These results are averages across all language pairs and data sources of training data for each length range shown. We find it best to select NMT data where the translations have between 0 and 10 tokens, with performance dropping as sentence length increases. This is true for both the GRAN and Avg models. We do the same filtering for the SimpWiki data, though the trend is not nearly as strong. This may be because machine translation quality drops as sentence length increases. This trend appears even though the datasets with higher ranges have more tokens of training data, since only the number of training sentence pairs is kept constant across configurations.