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    <abstract>Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.</abstract>
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%0 Conference Proceedings
%T Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph
%A Ji, Haozhe
%A Ke, Pei
%A Huang, Shaohan
%A Wei, Furu
%A Zhu, Xiaoyan
%A Huang, Minlie
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ji-etal-2020-language
%X Despite the success of generative pre-trained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integrate commonsense knowledge into generative pre-trained language models simply transfer relational knowledge by post-training on individual knowledge triples while ignoring rich connections within the knowledge graph. We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation. In this paper, we propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph. We empirically show that our model outperforms existing baselines on three text generation tasks that require reasoning over commonsense knowledge. We also demonstrate the effectiveness of the dynamic multi-hop reasoning module with reasoning paths inferred by the model that provide rationale to the generation.
%R 10.18653/v1/2020.emnlp-main.54
%U https://aclanthology.org/2020.emnlp-main.54
%U https://doi.org/10.18653/v1/2020.emnlp-main.54
%P 725-736

Markdown (Informal)

[Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph](https://aclanthology.org/2020.emnlp-main.54) (Ji et al., EMNLP 2020)

• Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (Ji et al., EMNLP 2020)

ACL

• Haozhe Ji, Pei Ke, Shaohan Huang, Furu Wei, Xiaoyan Zhu, and Minlie Huang. 2020. Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 725–736, Online. Association for Computational Linguistics.