---
title: "Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network"
authors:
  - "Sixing Wu"
  - "Minghui Wang"
  - "Dawei Zhang"
  - "Yang Zhou"
  - "Ying Li"
  - "Zhonghai Wu"
doi: "10.24963/ijcai.2021/546"
url: "https://ijcai.org/proceedings/2021/546"
pdf: "https://ijcai.org/proceedings/2021/0546.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3964-3970"
track: "Main Track"
keywords:
  - "Natural Language Processing: Natural Language Generation"
  - "Natural Language Processing: Dialogue"
---

# Knowledge-Aware Dialogue Generation via Hierarchical Infobox Accessing and Infobox-Dialogue Interaction Graph Network

Sixing Wu, Minghui Wang, Dawei Zhang, Yang Zhou, Ying Li, Zhonghai Wu

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3964-3970. DOI: [10.24963/ijcai.2021/546](https://doi.org/10.24963/ijcai.2021/546)

## Abstract

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to  access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.