  In multi-turn dialogue generation, responses are not only related to the
topic and background of the context but also related to words and phrases in
the sentences of the context. However, currently widely used hierarchical
dialog models solely rely on context representations from the utterance-level
encoder, ignoring the sentence representations output by the word-level
encoder. This inevitably results in a loss of information while decoding and
generating. In this paper, we propose a new dialog model X-ReCoSa to tackle
this problem which aggregates multi-scale context information for hierarchical
dialog models. Specifically, we divide the generation decoder into upper and
lower parts, namely the intention part and the generation part. Firstly, the
intention part takes context representations as input to generate the intention
of the response. Then the generation part generates words depending on sentence
representations. Therefore, the hierarchical information has been fused into
response generation. we conduct experiments on the English dataset DailyDialog.
Experimental results exhibit that our method outperforms baseline models on
both automatic metric-based and human-based evaluations.
