# Learning Neural Templates for Recommender Dialogue System

arXiv: https://arxiv.org/abs/2109.12302 · HTML: https://mlsanity.com/paper/2109.12302/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 61 references that are in the map.
- First posted on arXiv 25 Sep 2021.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://mlsanity.com/paper/1603.06393/) (2016)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion](https://mlsanity.com/paper/2007.04032/) (2020)
- [Conversational Recommender System](https://mlsanity.com/paper/1806.03277/) (2018)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [Collaborative Deep Learning for Recommender Systems](https://mlsanity.com/paper/1409.2944/) (2014)
- [Towards Topic-Guided Conversational Recommender System](https://mlsanity.com/paper/2010.04125/) (2020)
- [INSPIRED: Toward Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2009.14306/) (2020)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Interactive Path Reasoning on Graph for Conversational Recommendation](https://mlsanity.com/paper/2007.00194/) (2020)
- [Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning](https://mlsanity.com/paper/2105.09710/) (2021)
- [Global-to-local Memory Pointer Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1901.04713/) (2019)
- [RevCore: Review-augmented Conversational Recommendation](https://mlsanity.com/paper/2106.00957/) (2021)
- [Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue](https://mlsanity.com/paper/1909.03922/) (2019)
- [Pointing the Unknown Words](https://mlsanity.com/paper/1603.08148/) (2016)

## Built on by (most cited first, 22 of 22)

- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [User-Centric Conversational Recommendation with Multi-Aspect User Modeling](https://mlsanity.com/paper/2204.09263/) (2022)
- [Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information](https://mlsanity.com/paper/2112.08140/) (2021)
- [C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System](https://mlsanity.com/paper/2201.02732/) (2022)
- [TREA: Tree-Structure Reasoning Schema for Conversational Recommendation](https://mlsanity.com/paper/2307.10543/) (2023)
- [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023)
- [RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models](https://mlsanity.com/paper/2110.07477/) (2021)
- [A LLM-based Controllable, Scalable, Human-Involved User Simulator Framework for Conversational Recommender Systems](https://mlsanity.com/paper/2405.08035/) (2024)
- [U-NEED: A Fine-grained Dataset for User Needs-Centric E-commerce Conversational Recommendation](https://mlsanity.com/paper/2305.04774/) (2023)
- [Parameter-Efficient Conversational Recommender System as a Language Processing Task](https://mlsanity.com/paper/2401.14194/) (2024)
- [Follow Me: Conversation Planning for Target-driven Recommendation Dialogue Systems](https://mlsanity.com/paper/2208.03516/) (2022)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [Target-constrained Bidirectional Planning for Generation of Target-oriented Proactive Dialogue](https://mlsanity.com/paper/2403.06063/) (2024)
- [Multi-grained Hypergraph Interest Modeling for Conversational Recommendation](https://mlsanity.com/paper/2305.04798/) (2023)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [COLA: Improving Conversational Recommender Systems by Collaborative Augmentation](https://mlsanity.com/paper/2212.07767/) (2022)
- [Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation](https://mlsanity.com/paper/2310.18119/) (2023)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation](https://mlsanity.com/paper/2304.09093/) (2023)
- [Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition](https://mlsanity.com/paper/2402.18873/) (2024)
- [Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts](https://mlsanity.com/paper/2504.13655/) (2025)
- [Customized Conversational Recommender Systems](https://mlsanity.com/paper/2207.00814/) (2022)

## Abstract (arXiv)

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 urlhttps://github.com/jokieleung/NTRD.