# Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model
## 4 Results
### Case Study
Through the optimization of our framework, the model successfully identified the relevant question direction of \"SPACE system business\" in contrast to the erroneous response of \"Nobleman\" produced by GPT-2. This achievement underscores the effectiveness of our designed framework in identifying and selecting critical evidence with a high degree of accuracy. As shown in Table 7, KGEL generates a valid question for the answer, whereas GPT-2 generates a question with the wrong semantic relationship. This example is a compelling illustration, and it is imperative to highlight that it was selected randomly to accentuate the robustness of our model. The problem presented is a typical multi-hop challenge that involves intricate and diverse entity types, including time, personal names, workplace, nationality, and publishing houses. Such complexities necessitate sophisticated and intricate reasoning. Notably, our model, although phrased differently from the original question, was able to capture the essence of the problem. Conversely, the GPT-2 model produced paragraphs that were linguistically sound but devoid of any logical coherence. This underscores the pre-training model's capacity for linguistic fluency but indicates its inadequacy in complex reasoning tasks. Reviewing the generation of KGEL produces some thoughts for future direction:  
* The generation quality of multi-hop questions is far from desired;
* Incorporating external knowledge may help the model recognize the relationship between entities and answers;
* A copy mechanism has the potential to be introduced to help the model generate the shared semantic content in the target question for many words in question that come from context. \begin{table}
\begin{tabular}{|p{142.3 pt} p{142.3 pt}|} \hline
**Support Fact A:** & SpaceDev, a part of the “Space Systems Business” of Sierra Nevada Corporation, is prominent for its spaceflight and microsatellite work. \\
**Support Fact B:** & Sierra Nevada Corporation operates under the leadership of Chief Executive Officer, [PERSON] and President, [PERSON]. \\ \hline
**Answer:** & [PERSON] \\ \hline
**GPT-2:** & Who is the president of the company that operates Russian Li: Nobleman? \\
**KGEL:** & Who is the president of the corporation that is a member of the space systems business? \\
**Ground Truth:** & Who is the Chief Executive Officer of the corporation that owns Space Dev? \\ \hline \end{tabular}
\end{table} 
Table 6: Positive example 1. \begin{table}
\begin{tabular}{|p{142.3 pt} p{142.3 pt}|} \hline
**Support Fact A:** & In 2000, “Linux Format” called Qvwm “an unusually impressive imposter”. \\
**Support Fact B:** & Linux Format was the UK’s first Linux-specific magazine, and as of 2013 was the best-selling Linux title in the UK. \\ \hline
**Answer:** & An unusually impressive imposter \\ \hline
**GPT-2:** & What was the first Linux-such first linux-specific magazine? \\
**KGEL:** & What type of magazine called qvwm “an unusually impressive imposter”? \\
**Ground Truth:** & What did the UK’s first Linux-specific magazine call Qvwm? \\ \hline \end{tabular}
\end{table} 
Table 5: Negative example 2.