  Intent classification has been widely researched on English data with deep
learning approaches that are based on neural networks and word embeddings. The
challenge for Chinese intent classification stems from the fact that, unlike
English where most words are made up of 26 phonologic alphabet letters, Chinese
is logographic, where a Chinese character is a more basic semantic unit that
can be informative and its meaning does not vary too much in contexts. Chinese
word embeddings alone can be inadequate for representing words, and pre-trained
embeddings can suffer from not aligning well with the task at hand. To account
for the inadequacy and leverage Chinese character information, we propose a
low-effort and generic way to dynamically integrate character embedding based
feature maps with word embedding based inputs, whose resulting word-character
embeddings are stacked with a contextual information extraction module to
further incorporate context information for predictions. On top of the proposed
model, we employ an ensemble method to combine single models and obtain the
final result. The approach is data-independent without relying on external
sources like pre-trained word embeddings. The proposed model outperforms
baseline models and existing methods.
