From Table 1 we see that, while our method is consistently better than both the additive baseline and nonce2vec, removing stop-words from the additive baseline leads to stronger performance for more sentences.
following the success of recent neural (Mikolov et al., 2013) and matrixfactorization (Pennington et al., 2014) methods, researchers have sought to extend the approach to other text features, from subword elements to n-grams to sentences (Bojanowski et al., 2016;Poliak et al., 2017;Kiros et al., 2015).