  Generating character-level features is an important step for achieving good
results in various natural language processing tasks. To alleviate the need for
human labor in generating hand-crafted features, methods that utilize neural
architectures such as Convolutional Neural Network (CNN) or Recurrent Neural
Network (RNN) to automatically extract such features have been proposed and
have shown great results. However, CNN generates position-independent features,
and RNN is slow since it needs to process the characters sequentially. In this
paper, we propose a novel method of using a densely connected network to
automatically extract character-level features. The proposed method does not
require any language or task specific assumptions, and shows robustness and
effectiveness while being faster than CNN- or RNN-based methods. Evaluating
this method on three sequence labeling tasks - slot tagging, Part-of-Speech
(POS) tagging, and Named-Entity Recognition (NER) - we obtain state-of-the-art
performance with a 96.62 F1-score and 97.73% accuracy on slot tagging and POS
tagging, respectively, and comparable performance to the state-of-the-art 91.13
F1-score on NER.
