  Can a text classifier generalize well for datasets where the text length is
different? For example, when short reviews are sentiment-labeled, can these
transfer to predict the sentiment of long reviews (i.e., short to long
transfer), or vice versa? While unsupervised transfer learning has been
well-studied for cross domain/lingual transfer tasks, Cross Length Transfer
(CLT) has not yet been explored. One reason is the assumption that length
difference is trivially transferable in classification. We show that it is not,
because short/long texts differ in context richness and word intensity. We
devise new benchmark datasets from diverse domains and languages, and show that
existing models from similar tasks cannot deal with the unique challenge of
transferring across text lengths. We introduce a strong baseline model called
BaggedCNN that treats long texts as bags containing short texts. We propose a
state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that
introduces a two-way encoding scheme for short and long texts using multiple
training mechanisms. We test our models and find that existing models perform
worse than the BaggedCNN baseline, while LeTraNets outperforms all models.
