# Using a KG-Copy Network for Non-Goal Oriented Dialogues

arXiv: https://arxiv.org/abs/1910.07834 · HTML: https://mlsanity.com/paper/1910.07834/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generation](https://mlsanity.com/paper/2204.09149/) (2022)
- [Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers](https://mlsanity.com/paper/2103.16289/) (2021)
- [RoMe: A Robust Metric for Evaluating Natural Language Generation](https://mlsanity.com/paper/2203.09183/) (2022)
- [DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning](https://mlsanity.com/paper/2205.12662/) (2022)
- [UniPCM: Universal Pre-trained Conversation Model with Task-aware Automatic Prompt](https://mlsanity.com/paper/2309.11065/) (2023)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)

## Abstract (arXiv)

Non-goal oriented, generative dialogue systems lack the ability to generate answers with grounded facts. A knowledge graph can be considered an abstraction of the real world consisting of well-grounded facts. This paper addresses the problem of generating well grounded responses by integrating knowledge graphs into the dialogue systems response generation process, in an end-to-end manner. A dataset for nongoal oriented dialogues is proposed in this paper in the domain of soccer, conversing on different clubs and national teams along with a knowledge graph for each of these teams. A novel neural network architecture is also proposed as a baseline on this dataset, which can integrate knowledge graphs into the response generation process, producing well articulated, knowledge grounded responses. Empirical evidence suggests that the proposed model performs better than other state-of-the-art models for knowledge graph integrated dialogue systems.