  Though recent end-to-end neural models have shown promising progress on
Conversational Recommender System (CRS), two key challenges still remain.
First, the recommended items cannot be always incorporated into the generated
replies precisely and appropriately. Second, only the items mentioned in the
training corpus have a chance to be recommended in the conversation. To tackle
these challenges, we introduce a novel framework called NTRD for recommender
dialogue system that decouples the dialogue generation from the item
recommendation. NTRD has two key components, i.e., response template generator
and item selector. The former adopts an encoder-decoder model to generate a
response template with slot locations tied to target items, while the latter
fills in slot locations with the proper items using a sufficient attention
mechanism. Our approach combines the strengths of both classical slot filling
approaches (that are generally controllable) and modern neural NLG approaches
(that are generally more natural and accurate). Extensive experiments on the
benchmark ReDial show our NTRD significantly outperforms the previous
state-of-the-art methods. Besides, our approach has the unique advantage to
produce novel items that do not appear in the training set of dialogue corpus.
The code is available at \url{https://github.com/jokieleung/NTRD}.
