  Implicit discourse relation recognition is a challenging task as the relation
prediction without explicit connectives in discourse parsing needs
understanding of text spans and cannot be easily derived from surface features
from the input sentence pairs. Thus, properly representing the text is very
crucial to this task. In this paper, we propose a model augmented with
different grained text representations, including character, subword, word,
sentence, and sentence pair levels. The proposed deeper model is evaluated on
the benchmark treebank and achieves state-of-the-art accuracy with greater than
48% in 11-way and $F_1$ score greater than 50% in 4-way classifications for the
first time according to our best knowledge.
