Summary of Mp2d: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs, by Yerin Hwang et al.
MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs
by Yerin Hwang, Yongil Kim, Yunah Jang, Jeesoo Bang, Hyunkyung Bae, Kyomin Jung
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a long-standing challenge in developing on-topic dialogue systems: managing topic shifts within conversations. The authors propose Multi-Passage to Dialogue (MP2D), a data generation framework that creates conversational question-answering datasets with natural topic transitions. MP2D leverages relationships between entities in a knowledge graph to map the flow of topics within a dialogue, mirroring human conversation dynamics. It retrieves relevant passages and transforms them into dialogues using the passage-to-dialogue method. The authors demonstrate MP2D’s efficacy through quantitative and qualitative experiments, showcasing its ability to generate dialogue with natural topic shifts. Additionally, they introduce a novel benchmark for topic shift dialogues, TS-WikiDialog, which highlights the limitations of Large Language Models (LLMs) in handling topic shifts effectively. By training models on datasets generated by MP2D, the authors achieve performance improvements across diverse topic shift dialogue tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making conversations more realistic and natural-sounding. It proposes a new way to create conversation questions and answers that mimic how humans talk. The approach uses special relationships between words in a big database to understand how topics change during a conversation. This helps generate conversations that flow smoothly from one topic to another, just like people do when talking. The authors tested their method and showed it works well. They also created a new way to measure the quality of conversations that can switch topics easily. The results show that even very smart computer programs struggle with this task, but the new approach helps them do better. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering
