# Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model
## 5 Conclusions  
The present study proposes a novel approach to question generation, which leverages the benefits of a knowledge graph and mimics human reasoning. The proposed model, referred to as KGEL, incorporates an answer-aware graph reasoning module to improve the ability to identify key information from the context. Empirical evaluations on the HotpotQA dataset demonstrate the superiority of KGEL over baseline models, particularly in terms of the completeness and answerability of generated questions.  
In conclusion, this paper provides a promising approach for multi-hop question generation and highlights the potential for further improvement. Integrating external knowledge and incorporating a copy mechanism are identified as promising avenues for future research. These directions aim to enhance the ability of the model to recognize relationships across semantic content and generate shared semantic information in the target question.  
The results obtained from our experiment demonstrate relatively good performance compared to that of GPT-2 fine-tune. However, we must consider that our training data are limited to the Hotpot QA dataset. Given recent demonstrations of ChatGPT's remarkable progress and the sophisticated techniques employed therein, it is improbable that our model could achieve the complexity required for multi-hop question answering inference using only the Hotpot QA dataset. In contrast, ChatGPT's exceptional performance highlights the potential limitations of our model arising from insufficient data, and prompt training on more pertinent data would likely result in a more potent model.  
Z.L.: software, methodology, writing--original draft; Z.C.: methodology, writing--review and editing, validation; P.L.: investigation, writing--review and editing, validation; Y.Z.: writing--review and editing, supervision; S.L.: writing--review and editing, supervision, project administration. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the AI industrial technology innovation platform of Sichuan Province, grant number \"2020 ZHCG0002\".  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Support Fact A:** & Anne of Ingleside is a children’s novel by Canadian author [PERSON]. It was first published in July 1939 by McClelland and Stewart (Toronto) and the Frederick A. Stokes Company (New York). \\
**Support Fact B:** & [PERSON] ’1’; ’; ’2’; ’3’; ’4’; (30 November 1874-24 April 1942) published as [PERSON], was a Canadian author best known for a series of novels beginning in 1908 with “Anne of Green Gables” The central character, [PERSON], an orphaned girl, made [PERSON] famous in her lifetime and gave her an international following. \\ \hline
**Answer:** & [PERSON] \\ \hline
**GPT-2:** & Who wrote the central character’s novel in which the fictional female protagonist of the novel was first published in July 1939? \\
**KGEL:** & Who is the author best known for a series of novels about [PERSON] which was first published in July 1939, by McClelland and Stewart? \\
**Ground Truth:** & Wich children’s novelist who was first published in 1939 gained an internaional following writing about an orphaned girl named [PERSON]? \\ \hline \end{tabular}
\end{table}
Table 7: Positive example 2.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Model** & **Fluency** & **Answerability** & **Completeness** \\ \hline GPT-2 & 3.85 (\(\pm\)0.30) & 2.14 (\(\pm\)0.97) & 2.68 (\(\pm\)0.71) \\ KGEL & 4.43 (\(\pm\)0.41) & 2.92 (\(\pm\)0.76) & 3.28 (\(\pm\)0.34) \\ \hline Human & 4.87 (\(\pm\)0.13) & 4.96 (\(\pm\)0.21) & 4.86 (\(\pm\)0.26) \\ \hline \end{tabular}
\end{table}
Table 8: The results of human evaluation.