# Learning to Select Knowledge for Response Generation in Dialog Systems

arXiv: https://arxiv.org/abs/1902.04911 · HTML: https://mlsanity.com/paper/1902.04911/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 30 of 80)

- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [A Survey on Retrieval-Augmented Text Generation](https://mlsanity.com/paper/2202.01110/) (2022)
- [Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features](https://mlsanity.com/paper/2107.06963/) (2021)
- [Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://mlsanity.com/paper/2204.08128/) (2022)
- [KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection](https://mlsanity.com/paper/2310.09044/) (2023)
- [One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles](https://mlsanity.com/paper/2108.09355/) (2021)
- [Hindsight: Posterior-guided training of retrievers for improved open-ended generation](https://mlsanity.com/paper/2110.07752/) (2021)
- [A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems](https://mlsanity.com/paper/2204.06923/) (2022)
- [Semi-Supervised Variational Reasoning for Medical Dialogue Generation](https://mlsanity.com/paper/2105.06071/) (2021)
- [Personalized Large Language Model Assistant with Evolving Conditional Memory](https://mlsanity.com/paper/2312.17257/) (2023)
- [Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters](https://mlsanity.com/paper/2105.06232/) (2021)
- [Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation](https://mlsanity.com/paper/2212.11868/) (2022)
- [Adaptive Retrieval-Augmented Generation for Conversational Systems](https://mlsanity.com/paper/2407.21712/) (2024)
- [DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization](https://mlsanity.com/paper/2109.04673/) (2021)
- [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2405.17974/) (2024)
- [A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2109.04096/) (2021)
- [Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference](https://mlsanity.com/paper/2107.10931/) (2021)
- [Improving Personality Consistency in Conversation by Persona Extending](https://mlsanity.com/paper/2208.10816/) (2022)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [Generative Knowledge Selection for Knowledge-Grounded Dialogues](https://mlsanity.com/paper/2304.04836/) (2023)
- [Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation](https://mlsanity.com/paper/2102.09282/) (2021)
- [Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation](https://mlsanity.com/paper/2109.05487/) (2021)
- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs](https://mlsanity.com/paper/2206.07296/) (2022)
- [Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation](https://mlsanity.com/paper/2211.01587/) (2022)
- [Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals](https://mlsanity.com/paper/2107.08329/) (2021)
- [TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge](https://mlsanity.com/paper/2203.08517/) (2022)
- [Medical Dialogue Response Generation with Pivotal Information Recalling](https://mlsanity.com/paper/2206.08611/) (2022)
- [Opening up Minds with Argumentative Dialogues](https://mlsanity.com/paper/2301.06400/) (2023)
- [CADGE: Context-Aware Dialogue Generation Enhanced with Graph-Structured Knowledge Aggregation](https://mlsanity.com/paper/2305.06294/) (2023)

## Abstract (arXiv)

End-to-end neural models for intelligent dialogue systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by leveraging external knowledge. However, few previous work has focused on selecting appropriate knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model from learning to make full use of the knowledge. Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate the posterior distribution so that appropriate knowledge can be selected even without responses during the inference process. Compared with the previous work, our model can better incorporate appropriate knowledge in response generation. Experiments on both automatic and human evaluation verify the superiority of our model over previous baselines.