We propose a hierarchical self-attention mechanism to exploit the structural representation of conversations and learn a vector representation for the contextual utterances X i j−1 , ..., X i j−M .
the hierarchical self-attention follows two steps: 1) each utterance representation is computed using an utterance-level self-attention layer, and 2) a context representation is computed from M learned utterance representations using a context-level self-attention layer.