  Implicit discourse relation recognition is a challenging task due to the
absence of the necessary informative clue from explicit connectives. The
prediction of relations requires a deep understanding of the semantic meanings
of sentence pairs. As implicit discourse relation recognizer has to carefully
tackle the semantic similarity of the given sentence pairs and the severe data
sparsity issue exists in the meantime, it is supposed to be beneficial from
mastering the entire training data. Thus in this paper, we propose a novel
memory mechanism to tackle the challenges for further performance improvement.
The memory mechanism is adequately memorizing information by pairing
representations and discourse relations of all training instances, which right
fills the slot of the data-hungry issue in the current implicit discourse
relation recognizer. Our experiments show that our full model with memorizing
the entire training set reaches new state-of-the-art against strong baselines,
which especially for the first time exceeds the milestone of 60% accuracy in
the 4-way task.
