  Currently, open-domain generative dialog systems have attracted considerable
attention in academia and industry. Despite the success of single-turn dialog
generation, multi-turn dialog generation is still a big challenge. So far,
there are two kinds of models for open-domain multi-turn dialog generation:
hierarchical and non-hierarchical models. Recently, some works have shown that
the hierarchical models are better than non-hierarchical models under their
experimental settings; meanwhile, some works also demonstrate the opposite
conclusion. Due to the lack of adequate comparisons, it's not clear which kind
of models are better in open-domain multi-turn dialog generation. Thus, in this
paper, we will measure systematically nearly all representative hierarchical
and non-hierarchical models over the same experimental settings to check which
kind is better. Through extensive experiments, we have the following three
important conclusions: (1) Nearly all hierarchical models are worse than
non-hierarchical models in open-domain multi-turn dialog generation, except for
the HRAN model. Through further analysis, the excellent performance of HRAN
mainly depends on its word-level attention mechanism; (2) The performance of
other hierarchical models will also obtain a great improvement if integrating
the word-level attention mechanism into these models. The modified hierarchical
models even significantly outperform the non-hierarchical models; (3) The
reason why the word-level attention mechanism is so powerful for hierarchical
models is because it can leverage context information more effectively,
especially the fine-grained information. Besides, we have implemented all of
the models and already released the codes.
