  Open-domain multi-turn conversations mainly have three features, which are
hierarchical semantic structure, redundant information, and long-term
dependency. Grounded on these, selecting relevant context becomes a challenge
step for multi-turn dialogue generation. However, existing methods cannot
differentiate both useful words and utterances in long distances from a
response. Besides, previous work just performs context selection based on a
state in the decoder, which lacks a global guidance and could lead some focuses
on irrelevant or unnecessary information. In this paper, we propose a novel
model with hierarchical self-attention mechanism and distant supervision to not
only detect relevant words and utterances in short and long distances, but also
discern related information globally when decoding. Experimental results on two
public datasets of both automatic and human evaluations show that our model
significantly outperforms other baselines in terms of fluency, coherence, and
informativeness.
