# Difference-aware Knowledge Selection for Knowledge-grounded Conversation Generation

arXiv: https://arxiv.org/abs/2009.09378 · HTML: https://mlsanity.com/paper/2009.09378/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation](https://mlsanity.com/paper/2202.13047/) (2022)
- [DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization](https://mlsanity.com/paper/2109.04673/) (2021)
- [Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation](https://mlsanity.com/paper/2109.06513/) (2021)
- [Generative Knowledge Selection for Knowledge-Grounded Dialogues](https://mlsanity.com/paper/2304.04836/) (2023)
- [Enhanced Knowledge Selection for Grounded Dialogues via Document Semantic Graphs](https://mlsanity.com/paper/2206.07296/) (2022)
- [EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator](https://mlsanity.com/paper/2301.00972/) (2023)
- [Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs](https://mlsanity.com/paper/2302.11849/) (2023)
- [CorefDiffs: Co-referential and Differential Knowledge Flow in Document Grounded Conversations](https://mlsanity.com/paper/2210.02223/) (2022)
- [Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation](https://mlsanity.com/paper/2310.08943/) (2023)
- [Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models](https://mlsanity.com/paper/2110.02067/) (2021)
- [Lexical Knowledge Internalization for Neural Dialog Generation](https://mlsanity.com/paper/2205.01941/) (2022)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System](https://mlsanity.com/paper/2104.11454/) (2021)
- [CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations](https://mlsanity.com/paper/2403.01848/) (2024)

## Abstract (arXiv)

In a multi-turn knowledge-grounded dialog, the difference between the knowledge selected at different turns usually provides potential clues to knowledge selection, which has been largely neglected in previous research. In this paper, we propose a difference-aware knowledge selection method. It first computes the difference between the candidate knowledge sentences provided at the current turn and those chosen in the previous turns. Then, the differential information is fused with or disentangled from the contextual information to facilitate final knowledge selection. Automatic, human observational, and interactive evaluation shows that our method is able to select knowledge more accurately and generate more informative responses, significantly outperforming the state-of-the-art baselines. The codes are available at https://github.com/chujiezheng/DiffKS.