Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Table 1: Performances of the Seq2Seq models with and without attention on the RCV1-v2 test set, where HL, P, R, and F1 refer to hamming loss, micro-precision, micro-recall and micro-F1. The symbol “+” indicates that the higher the better, while the symbol “-” indicates that the lower the better.
['[BOLD] Models', '[BOLD] HL(-)', '[BOLD] P(+)', '[BOLD] R(+)', '[BOLD] F1(+)']
This can be a proof that the conventional attention mechanism does not play a significant role in the improvement of the Seq2Seq model’s performance. We hypothesize that the conventional attention mechanism does not meet the requirements of multi-label text classification. A common sense for such a classification task is that the classification should be based on the salient ideas of the source text. The semantic units, instead of word-level information, mainly determine that the text can be classified into the target categories “youth” and “sports”. For each of a variety of texts, there are always semantic units that construct the semantic meaning of the whole text. Regarding an automatic system for multi-label text classification, the system should be able to extract the semantic units in the source text for better performance in classification. Therefore, we propose our model to tackle this problem.