ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Table 15: Difference in correlation (Pearson’s r×100) between “Word , Trigram” and InferSent models trained on two different datasets: AllNLI and SNLI. The first row is the mean difference across all 25 datasets, then the following rows show differences on three individual datasets that are comprised of captions. The InferSent models are much closer to our model on the caption datasets than overall.
['Data', 'AllNLI', 'SNLI']
Effect of Training Domain on InferSent We performed additional experiments to investigate the impact of training domain on downstream tasks. We first compare the performance of our “Word , Trigram (concatenation)” model to the InferSent SNLI and AllNLI models on all STS tasks from 2012-2016. We then compare the overall mean with that of the three caption STS datasets within the collection. The InferSent models are much closer to our Word , Trigram model on the caption datasets than overall, and InferSent trained on SNLI shows the largest difference between its overall performance and its performance on caption data.