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 https://github.com/jokieleung/NTRD.

提出了一种名为 NTRD 的新型框架，旨在解决基于对话式推荐系统中推荐项目无法精确恰当地纳入生成的回复以及只有出现在训练语料库中的项目才有机会在对话中被推荐的难题。 NTRD 具有两个关键组件，即响应模板生成器和项选择器，并结合了传统的槽填充方法和现代神经自然语言生成方法的优点。实验表明，我们的 NTRD 明显优于以前的最先进方法，并具有产生在对话语料库的训练集中未出现的新项目的独特优势。