Learning Neural Templates for Recommender Dialogue System

TL;DR

This paper introduces NTRD, a novel neural framework for recommender dialogue systems that improves item incorporation and enables recommending novel items outside the training data, outperforming previous methods.

Contribution

The paper proposes a decoupled neural template approach with response template generation and item selection, enhancing controllability and novelty in recommender dialogue systems.

Findings

NTRD significantly outperforms previous state-of-the-art methods on ReDial.

The approach can generate novel items not seen in training data.

NTRD effectively combines classical slot filling with neural NLG techniques.

Abstract

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}.