Learning Paraphrastic Sentence Embeddingsfrom Back-Translated Bitext
Table 6: Length filtering test results after tuning length ranges on development data (averaged over languages and data sources for the NMT rows). Results are on STS datasets (Pearson’s r×100).
['Filtering Method', 'NMT GRAN', 'NMT Avg', 'SimpWiki GRAN', 'SimpWiki Avg']
We then tune the length range using our development data, considering the following length ranges: [0,10], [0,15], [0,20], [0,30], [0,100], [10,20], [10,30], [10,100], [15,25], [15,30], [15,100], [20,30], [20,100], [30,100]. We tune over ranges as well as language, data source, and stopping epoch, each time training on 24,000 sentence pairs. We compare to a baseline that draws a random set of data, showing that length-based filtering leads to gains of nearly half a point on average across our test sets. The tuned length ranges are short for both NMT and SimpWiki.