A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Table 3: Results of our model on different scenarios
['[BOLD] Model', '[BOLD] Multi-Cardinality SST-1', '[BOLD] Multi-Cardinality SST-2', '[BOLD] Multi-Cardinality IMDB', '[BOLD] Multi-Domain Books', '[BOLD] Multi-Domain DVDs', '[BOLD] Multi-Domain Electronics', '[BOLD] Multi-Domain Kitchen', '[BOLD] Multi-Objective IMDB', '[BOLD] Multi-Objective RN', '[BOLD] Multi-Objective QC']
Our model obtains better performances in Multi-Domain scenario with an average improvement of 4.5%, where datasets are product reviews on different domains with similar sequence lengths and the same class number, thus producing stronger correlations. Multi-Cardinality scenario also achieves significant improvements of 2.77% on average, where datasets are movie reviews with different cardinalities.