GNET-QG: graph network for multi-hop question generation
| dc.contributor.author | Jamshidi, Samin | |
| dc.contributor.author | University of Lethbridge. Faculty of Arts and Science | |
| dc.contributor.supervisor | Chali, Yllias | |
| dc.date.accessioned | 2024-09-25T22:12:50Z | |
| dc.date.available | 2024-09-25T22:12:50Z | |
| dc.date.issued | 2024-08-28 | |
| dc.degree.level | Masters | |
| dc.description.abstract | Question generation involves crafting questions based on a given input context and accompanying answers. While recent advancements in sequence-to-sequence models have proven successful in generating natural language questions, there is an increasing need for models capable of handling more intricate contexts to produce detailed questions. Multi-hop question generation, which is more challenging, aims to establish connections between multiple facts from diverse input contexts to formulate questions. In our research, we study the utilization of a Graph Attention Network (GAT) and a BART model for multi-hop question generation, Our proposed model, is GNET-QG (Graph Network for Multi-Hop Question Generation). GNET-QG efficacy is assessed on the HotpotQA dataset using metrics such as METEOR, BLEU, and ROUGE, showcasing an enhancement over previous methodologies. | |
| dc.embargo | Yes | |
| dc.identifier.uri | https://hdl.handle.net/10133/6912 | |
| dc.language.iso | en | |
| dc.publisher | Lethbridge, Alta. : University of Lethbridge, Dept. of Computer Science | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Arts and Science | |
| dc.relation.ispartofseries | Thesis (University of Lethbridge. Faculty of Arts and Science) | |
| dc.subject | Question generation | |
| dc.subject | Multi-hop question generation | |
| dc.subject | Formulating questions | |
| dc.subject | Natural language questions | |
| dc.subject.lcsh | Dissertations, Academic | |
| dc.title | GNET-QG: graph network for multi-hop question generation | |
| dc.type | Thesis |