Our work leverages the attention-based selfexplaining method<cite> (Lin et al., 2017)</cite> , as shown in Figure 1 . First, our text encoder ( §3) formulates an input text into a list of basic units, learning a vector representation for each, where the basic units can be words, phrases, or arbitrary ngrams. Then, the attention mechanism is leveraged over all basic units, and sums up all unit representations based on the attention weights {α 1 , ..., α n }. Eventually, the attention weight α i will be used to reveal how important a basic unit h i is. The last prediction layer takes the fixed-length text representation t as input, and makes the final prediction.