# Multi-Hop Question Generation with Knowledge Graph-Enhanced Language Model
## 1 Introduction
Multi-hop QG needs to perform reasoning from evidence (a) to (i), whereas single-hop QG generates questions from one sentence that explicitly states the answer. The question generated by GPT-2 (**left**) is irrelevant to the context. * The utilization of a GPT-2 model for encoding the context and answer allows for better semantic understanding, leveraging the pre-trained knowledge from its large-scale corpus. * The bidirectional attention mechanism in the answer-aware GAT enables the model to dynamically capture the interactions between the answer and the knowledge graph, thus leading to a more comprehensive and accurate aggregation of the relevant information. Furthermore, the Graph2 Context encoding allows the context representations to be updated with the knowledge derived from the knowledge graph, providing the model with a better semantic understanding of the context and the answer. These innovations enable the model to have a more thorough understanding of the context and answer, and, therefore, generate higher quality questions. * Additionally, the multi-head attention generation module adopts a multi-head attention mechanism to capture the relationships among the latent representations, resulting in a more comprehensive understanding of the context. Furthermore, the module generates questions based on the enhanced understanding of the context, ensuring the fluency and logicality of the generated questions. The technical details of the module and the entire approach are thoroughly described in Section 2 of the paper. Neural networks are commonly regarded as black boxes that require experimental validation to justify their effectiveness. Theoretical and mathematical explanations are lacking in this paper, but our results demonstrate that the framework we envisioned can improve performance. To address, identify, and integrate crucial evidence from sentences and complex reasoning over scattered evidence, we propose a novel approach that combines entity recognition models and graph neural networks to enhance evidence discovery and sorting. In line with human cognition, we hypothesize that evidence is often expressed as a concrete noun. Therefore, upon identifying an entity, we expect to uncover evidence that is pertinent to the problem. Our graph neural network model is designed to function like a chain of evidence (it is the answer-aware GAT in Figure 2), connecting information among entities. By adopting a human-like reasoning process, we anticipate the model to identify key evidence about answers (it is the bi-attention in Figure 2) and integrate them to capture relevant information more accurately. To generate fluency, we choose a pre-trained language model to read and generate the language. Our proposed framework is founded on a straightforward approach that comprises three fundamental steps. First, a pre-trained model is leveraged to comprehend the text. Second, a graph neural network is employed to enhance the interaction of entities within the text. Finally, the information gleaned from the preceding two steps is amalgamated and organized. It is important to note that our framework is not constrained to any specific model. Figure 2: Framework of KGEL. It consists of three major modules: a context understanding module, a reasoning module, and a generation module. Our contributions are summarized as follows: The proposed method for multi-hop question generation offers a novel approach that mirrors the human multi-hop questioning process. Our approach is composed of three phases: (1) context understanding through the utilization of a pre-trained GPT-2 language model, (2) integration of information and reasoning through a knowledge graph and an answer-aware dynamic GAT, and (3) QG through a multi-head attention generation module with enhanced latent representations.