[
Table 1: Transfer loss (TL) of different classifiers for each transfer task for all datasets described in Section 2.1.
['[EMPTY]', 'Mov_en', 'Mov_en', 'Res_en', 'Res_en', 'Mov_ko', 'Mov_ko']
To motivate the non-triviality of such transfer, we train an out-channel (OC) classifier that uses short texts to predict long texts, and an in-channel (IC) classifier that uses long texts on both training and prediction. We also experiment conversely. We use three kinds of classifiers: bag-of-words (BoW), convolutional neural networks (CNN; kim2014convolutional), and BERT multilingual (devlin2018bert). We calculate the transfer loss (TL; glorot2011domain), which is the difference between the out-channel and in-channel classifier errors (i.e., TL=0 means trivially transferable). Our first contribution is thus to define a new task called Cross Length Transfer (CLT). Our second contribution is to show that models from similar tasks Finally, we present two new models specifically for CLT: a strong baseline called BaggedCNN that treats long texts as bags containing short texts, and a state-of-the-art CLT model called Length Transfer Networks (LeTraNets). LeTraNets enables a two-way encoding scheme using multiple training mechanims, and accepts both short and long text inputs, where one such input is created artificially through concatenation or segmentation.