@inproceedings{liu-etal-2019-knowledge,
title = "Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs",
author = "Liu, Zhibin and
Niu, Zheng-Yu and
Wu, Hua and
Wang, Haifeng",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1187",
doi = "10.18653/v1/D19-1187",
pages = "1782--1792",
abstract = "Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.",
}
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<abstract>Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.</abstract>
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%0 Conference Proceedings
%T Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
%A Liu, Zhibin
%A Niu, Zheng-Yu
%A Wu, Hua
%A Wang, Haifeng
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F liu-etal-2019-knowledge
%X Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous works. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.
%R 10.18653/v1/D19-1187
%U https://aclanthology.org/D19-1187
%U https://doi.org/10.18653/v1/D19-1187
%P 1782-1792
Markdown (Informal)
[Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs](https://aclanthology.org/D19-1187) (Liu et al., EMNLP-IJCNLP 2019)
ACL